Industrial kiln non-contact temperature monitoring system and method based on artificial intelligence

Abstract

The invention relates to the technical field of temperature measurement, in particular to an industrial kiln non-contact temperature monitoring system and method based on artificial intelligence, and the method comprises the steps: carrying out the matching processing of the real-time air environment characteristic data of an industrial plant area and the temperature monitoring air environment influence data, and generating the temperature monitoring influence data of an industrial kiln; real temperature data metering processing is carried out on the collected temperature data of the industrial kiln monitoring area, real temperature data of the industrial kiln monitoring area are generated, the real-time operation condition of the industrial kiln is analyzed according to the real temperature data of the industrial kiln monitoring area, and color display data of the industrial kiln monitoring area are generated; according to the method, the industrial kiln temperature monitoring platform is established, kiln temperature monitoring result display data are established, finally kiln temperature monitoring result data are established and pushed to the industrial kiln temperature monitoring platform, non-contact accurate temperature measurement of the industrial kiln is achieved, and meanwhile intelligent monitoring is conducted on the real-time temperature data of the industrial kiln.

Description

Technical Field

[0001] The present invention relates to the field of temperature measurement technology, and in particular to an industrial furnace non-contact temperature monitoring system and method based on artificial intelligence. Background Art

[0002] Industrial kilns are used in many industrial fields, and the monitoring results of their internal temperature play an important role in the industrial production process. However, traditional temperature monitoring methods have many limitations. It is often impossible to obtain reliable temperature monitoring results in the high-temperature working environment of industrial kilns. Therefore, non-contact temperature monitoring technology has gradually become an important technical means for industrial kiln temperature monitoring.

[0003] The Chinese invention patent with publication number CN101762327B introduces an infrared temperature monitoring method and system for electrified railway contact network. Visible light images with contact network information and thermal images with temperature data are collected in real time through an infrared temperature monitoring device, and the collected image data is transmitted to a server, decompressed by the on-board software centralized control system in the server, and then displayed in real time through a terminal display device. When abnormal temperature occurs, the system will sound an alarm to ensure the normal operation of the electric locomotive while monitoring the contact network in real time. However, it is impossible to perform comprehensive analysis of multiple sets of collected data to obtain the real-time temperature of the contact network. It is difficult to guarantee the impact of data collection errors and aging of the monitoring device, and it is impossible to ensure the accuracy of the analysis results. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In order to solve the deficiencies in the background technology, the present invention provides an industrial kiln non-contact temperature monitoring system and method based on artificial intelligence, which realizes non-contact precise temperature measurement of the industrial kiln and intelligently monitors the real-time temperature data of the industrial kiln.

[0006] (II) Technical solution

[0007] A non-contact temperature monitoring method for an industrial kiln based on artificial intelligence comprises the following steps: S1. Collect real-time air environment characteristic data of industrial plants; S2. Establish temperature monitoring air environment impact data, perform air environment feature data matching processing on the real-time air environment feature data of the industrial plant area and the temperature monitoring air environment impact data, and generate industrial kiln temperature monitoring impact data; S3, collect temperature data of the kiln monitoring area; S4, performing real temperature data measurement processing according to the kiln temperature monitoring influence data and the kiln monitoring area temperature data to generate the real temperature data of the industrial kiln monitoring area; S5. Analyze the real-time operation status of the industrial kiln based on the real temperature data of the kiln monitoring area, and generate color display data of the industrial kiln monitoring area according to the analysis results; S6. Constructing kiln temperature monitoring result display data according to the kiln monitoring area color display data; S7. Construct kiln temperature monitoring result data based on the real temperature data of the kiln monitoring area and the kiln monitoring result display data, and push it to the industrial kiln temperature monitoring platform.

[0008] The present invention matches the real-time air environment characteristic data of the industrial plant with the air environment impact data monitored by temperature, generates the temperature monitoring impact data of the industrial kiln, and performs real temperature data measurement processing with the collected temperature data of the industrial kiln monitoring area to generate the real temperature data of the industrial kiln monitoring area. The real-time operation of the industrial kiln is analyzed according to the real temperature data of the kiln monitoring area, the color display data of the industrial kiln monitoring area is generated, and the kiln temperature monitoring result display data is constructed. Finally, the kiln temperature monitoring result data is constructed and pushed to the industrial kiln temperature monitoring platform, so as to realize non-contact precise temperature measurement of the industrial kiln and perform intelligent monitoring of the real-time temperature data of the industrial kiln.

[0009] Preferably, the specific steps for collecting real-time air environment characteristic data of industrial plants are as follows: S11. The air environment characteristic data in the working area of ​​the target industrial kiln are collected in real time through an air environment monitoring device to obtain real-time air environment characteristic data A of the industrial plant area. The air environment monitoring device includes a temperature sensor, a humidity sensor, a meteorological sensor, a particle sensor and a gas sensor. The air environment characteristic data includes but is not limited to gas composition data, total suspended particulate matter concentration, air temperature, air relative humidity, wind direction data and wind speed data.

[0010] Preferably, the temperature monitoring air environment impact data is established, and the air environment feature data matching processing is performed on the real-time air environment feature data of the industrial plant area and the temperature monitoring air environment impact data, and the specific steps of generating the industrial kiln temperature monitoring impact data are as follows: S21. Establish temperature monitoring air environment impact data set B={b1,b2,…,b i ,…,b k}, where b i represents the temperature monitoring influence coefficient corresponding to the i-th type of air environment characteristic data, and k represents the total number of temperature monitoring air environment influence data; S22, using the Pelican optimization algorithm, the real-time air environment characteristic data A of the industrial plant area is matched with the temperature monitoring air environment impact data in the temperature monitoring air environment impact data set to generate an industrial kiln temperature monitoring impact data set. ; S221. Construct an air environment feature search for a pelican population, set the air environment feature search for a pelican population size to N, the current number of iterations to t, and the maximum number of iterations to t. max And the search space dimension of temperature monitoring air environment impact data is P; The temperature monitoring air environment impact data set is used as the temperature monitoring air environment impact data search space, and N temperature monitoring air environment impact data are randomly generated in the temperature monitoring air environment impact data search space, each temperature monitoring air environment impact data corresponds to an air environment feature search pelican individual in the air environment feature search pelican population, and the initial position set of the air environment feature search pelican population is initialized as X={X1,X2,…,X i ,…,X N}, where X i represents the initial position of the i-th air environment feature search pelican individual in the air environment feature search pelican population; S222, calculate the fitness function value of each individual pelican in the pelican population, arrange the pelican individuals in the pelican population from large to small according to the fitness function value, and select the pelican individual with the highest fitness function value as the current optimal individual; the fitness function formula is as follows: , Among them, Fit i represents the fitness function value of the pelican individual searching for the i-th air environment feature, x i represents the deviation value between the air environment characteristic data corresponding to the temperature monitoring air environment impact data corresponding to the i-th pelican individual and the real-time air environment characteristic data of the industrial plant area, Indicates the correction value; S223, exploration stage, each individual pelican in the pelican population locks the prey position in the temperature monitoring air environment impact data search space, and moves towards the prey position to update the position, and the position of the prey in the temperature monitoring air environment impact data search space is randomly generated; the position update formula is as follows: , in, and X ijThey respectively represent the position of the i-th air environment feature search pelican individual in the air environment feature search pelican population after and before the position update in the j-dimensional temperature monitoring air environment impact data search space, α represents a random integer uniformly distributed between [1,2], and Z j represents the prey position locked by the i-th air environment feature search pelican individual in the air environment feature search pelican population in the j-dimensional temperature monitoring air environment impact data search space, rand1 and rand2 represent random numbers uniformly distributed between (0,1), respectively, Fit z represents the fitness function value of the prey; S224, development stage, each individual pelican in the pelican population uses a surface flight strategy to hunt more prey in the temperature monitoring air environment impact data search space to update the position; the position update formula is as follows: , Where R represents a random integer with a value of 0 or 2, and rand2 represents a random number that follows a uniform distribution between (0,1); S225, calculating the fitness function value of each individual air environment feature search pelican in the air environment feature search pelican population after the position is updated, if the fitness function value of the individual air environment feature search pelican after the position is updated is less than the original fitness function value, then the new position is used to replace the original position; otherwise, the original position is retained; Rearranging the air environment characteristic searching pelican individuals in the air environment characteristic searching pelican population from large to small according to the fitness function value, and selecting the air environment characteristic searching pelican individual with the highest fitness function value as the new current optimal individual; S226: Determine whether the current number of iterations t is less than the maximum number of iterations t max , if the current number of iterations t is less than the maximum number of iterations t max , then the current iteration number t is increased by 1, and the process returns to S223; otherwise, the current optimal individual is taken as the global optimal solution, the temperature monitoring air environment impact data corresponding to the global optimal solution is output and the data is marked, and the industrial kiln temperature monitoring impact data is generated. .

[0011] The Pelican optimization algorithm is used to match the real-time air environment characteristic data of the industrial plant with the temperature monitoring air environment impact data, and the industrial kiln temperature monitoring impact data that matches the current situation is accurately matched, providing a reliable data basis for the subsequent calculation of real temperature data. At the same time, the Pelican optimization algorithm can improve the speed and efficiency of the matching process and reduce the response time for obtaining temperature measurement results.

[0012] Preferably, the specific steps for collecting temperature data of the kiln monitoring area are as follows: S31, setting a temperature data collection period, and evenly dividing the temperature data collection period into a number of temperature data collection time points, to obtain a temperature data collection time point set C = {c1, c2, ..., c i ,…,c l}, where c i represents the i-th temperature data collection time point obtained by evenly dividing the temperature data collection period, and l represents the total number of temperature data collection time points; S32, evenly divide the industrial kiln temperature monitoring area into several industrial kiln temperature monitoring sub-areas, and obtain the industrial kiln temperature monitoring sub-area set D = {d1, d2, ..., d i ,…,d p}, where d i represents the i-th industrial kiln temperature monitoring sub-area obtained by evenly dividing the industrial kiln temperature monitoring area, and p represents the total number of industrial kiln temperature monitoring sub-areas; S33, allocating an infrared temperature measuring device to each industrial kiln temperature monitoring sub-area in the kiln temperature monitoring sub-area set, and using the infrared temperature measuring device to collect temperature data of the center position of the corresponding industrial kiln temperature monitoring sub-area in real time at each temperature data collection time point in the temperature data collection time point set, and obtaining the industrial kiln monitoring area temperature data matrix E as follows: , Among them, e ij It represents the temperature data obtained by real-time collection of the center position of the j-th industrial kiln temperature monitoring sub-area at the i-th temperature data collection time point by the infrared temperature measuring device.

[0013] Preferably, the specific steps of performing real temperature data measurement processing according to the kiln temperature monitoring influence data and the kiln monitoring area temperature data to generate the real temperature data of the industrial kiln monitoring area are as follows: S41, the furnace temperature monitoring influence data is calculated according to the real temperature data calculation formula The real temperature data measurement processing is performed on the industrial kiln monitoring area temperature data in the kiln monitoring area temperature data matrix E to generate the industrial kiln monitoring area real temperature data matrix as follows: , in, It represents the real temperature data of the industrial kiln monitoring area obtained by real-time temperature data measurement processing of the temperature data collected by the infrared temperature measuring device at the center of the jth industrial kiln temperature monitoring sub-area at the i-th temperature data collection time point; the real temperature data calculation formula is as follows: , Among them, D i It represents the distance between the center position of the temperature monitoring sub-area of ​​the i-th industrial kiln and its corresponding infrared temperature measuring device, and D0 represents the unit distance.

[0014] The real temperature data calculation formula is used to perform real temperature data measurement processing on the kiln temperature monitoring influence data and the kiln monitoring area temperature data, so as to achieve accurate correction of the collected real-time temperature data, effectively reduce the influence of the air environment and the temperature measurement distance on the non-contact temperature measurement of the industrial kiln, and ensure the accuracy and reliability of the acquired real temperature data.

[0015] Preferably, the real-time operation status of the industrial kiln is analyzed based on the real temperature data of the kiln monitoring area, and the specific steps of generating the color display data of the industrial kiln monitoring area according to the analysis results are as follows: S51, set the real temperature threshold, and transform the real temperature data matrix of the kiln monitoring area into The real temperature data of each industrial kiln monitoring area in the temperature monitoring sub-area are numerically compared with the real temperature threshold, and the total number of industrial kiln monitoring area real temperature data corresponding to each industrial kiln temperature monitoring sub-area greater than the real temperature threshold is counted to obtain the industrial kiln monitoring area abnormal temperature number set F={f1,f2,…,f i ,…,f p}, where f i Indicates the total number of real temperature data obtained by collecting temperature data for the i-th industrial furnace temperature monitoring sub-area that are greater than the real temperature threshold; S52, based on the abnormal temperature number of the industrial kiln monitoring area in the kiln monitoring area abnormal temperature number set, calculate the abnormal temperature ratio of each industrial kiln temperature monitoring sub-area, and obtain the industrial kiln monitoring area abnormal temperature ratio set. ,in, represents the abnormal temperature ratio of the temperature monitoring sub-area of ​​the i-th industrial kiln, and ; S53, setting the first threshold value as η1 and the second threshold value as η2, comparing the abnormal temperature ratios of each industrial kiln monitoring area in the kiln monitoring area abnormal temperature ratio set with the first threshold value η1 and the second threshold value η2 in turn, and generating an industrial kiln monitoring area color display data set according to the numerical comparison results. ,in, Indicates the color display data of the industrial kiln monitoring area corresponding to the i-th industrial kiln temperature monitoring sub-area; Generating the industrial furnace monitoring area color display data set according to the numerical comparison result comprises the following steps: S531, selecting the abnormal temperature ratio of the i-th industrial furnace monitoring area and comparing it with the first threshold η1 and the second threshold η2; like , it means that the i-th industrial kiln temperature monitoring sub-area is in a non-working state, and the color display data of the industrial kiln monitoring area corresponding to the i-th industrial kiln temperature monitoring sub-area is output as black; like , it means that the ith industrial kiln temperature monitoring sub-area is in a low temperature state, and the color display data of the industrial kiln monitoring area corresponding to the ith industrial kiln temperature monitoring sub-area is output as yellow; like , it means that the ith industrial kiln temperature monitoring sub-area is at room temperature, and the color display data of the industrial kiln monitoring area corresponding to the ith industrial kiln temperature monitoring sub-area is output as orange; like , it means that the temperature monitoring sub-area of ​​the i-th industrial kiln is in a high temperature state, and the color display data of the industrial kiln monitoring area corresponding to the temperature monitoring sub-area of ​​the i-th industrial kiln is output as red; S532, repeating the operation in S531 until all the abnormal temperature ratios of the industrial kiln monitoring areas in the kiln monitoring area abnormal temperature ratio set are traversed, and a color display data set of the industrial kiln monitoring area is generated.

[0016] By counting the number of abnormal temperatures in each sub-area within a single cycle and calculating the abnormal temperature ratio, the abnormal temperature ratio is numerically compared with the set threshold value, the real-time temperature situation of each sub-area is analyzed, and the corresponding color display data is set according to different real-time temperature conditions. The real-time temperature characteristics are highlighted by different colors, and the real-time monitoring results of each sub-area are intuitively displayed, realizing the visualization of the non-contact temperature monitoring results of the industrial kiln. At the same time, the real-time temperature situation of each sub-area is analyzed through the collected multiple sets of data to ensure the comprehensiveness and accuracy of the temperature analysis results.

[0017] Preferably, the specific steps of constructing kiln temperature monitoring result display data based on the kiln monitoring area color display data are as follows: S61. Construct graphic feature data of the industrial kiln monitoring area, fill the graphic feature data of the kiln monitoring area with color according to the corresponding industrial kiln temperature monitoring sub-area position using the industrial kiln monitoring area color display data in the kiln monitoring area color display data set, and generate kiln temperature monitoring result display data Q.

[0018] Preferably, the specific steps of constructing kiln temperature monitoring result data based on the real temperature data of the kiln monitoring area and the kiln monitoring result display data, and pushing it to the industrial kiln temperature monitoring platform are as follows: S71, the real temperature data matrix of the kiln monitoring area Combine the data with the kiln monitoring result display data Q to construct the kiln temperature monitoring result data ; S72, transmitting the kiln temperature monitoring result data through the Internet of Things communication network Push to the industrial kiln temperature monitoring platform and display on the display screen.

[0019] The present invention also includes an artificial intelligence-based non-contact temperature monitoring system for industrial kilns, including an industrial plant area real-time air environment characteristic data acquisition module, an industrial kiln temperature monitoring influence data matching module, an industrial kiln monitoring area temperature data acquisition module, an industrial kiln monitoring area real temperature data metering module, an industrial kiln real-time operation status analysis module, a kiln temperature monitoring result display data construction module, and a kiln temperature monitoring result display module; The industrial plant area real-time air environment characteristic data acquisition module collects the air environment characteristic data in the target industrial kiln working area in real time through the air environment monitoring device to obtain the real-time air environment characteristic data of the industrial plant area; The kiln temperature monitoring impact data matching module performs air environment feature data matching processing on the real-time air environment feature data of the industrial plant area and the established temperature monitoring air environment impact data through the Pelican optimization algorithm to generate industrial kiln temperature monitoring impact data; The kiln monitoring area temperature data acquisition module collects the temperature data of the center position of the corresponding industrial kiln temperature monitoring sub-area in real time at each temperature data acquisition time point through an infrared temperature measuring device to obtain the temperature data of the industrial kiln monitoring area; The real temperature data metering module of the kiln monitoring area performs real temperature data metering processing on the kiln temperature monitoring influence data and the kiln monitoring area temperature data according to the real temperature data calculation formula to generate the real temperature data of the industrial kiln monitoring area; The real-time operation analysis module of the kiln compares the real temperature data of the kiln monitoring area with the real temperature threshold value, counts the total number of real temperature data of the kiln monitoring area that are greater than the real temperature threshold value, calculates the abnormal temperature ratio of the industrial kiln monitoring area, compares the abnormal temperature ratio of the kiln monitoring area with the set first threshold value and second threshold value, and generates color display data of the industrial kiln monitoring area according to the comparison result; The kiln monitoring result display data construction module performs color filling on the constructed industrial kiln monitoring area graphic feature data based on the kiln monitoring area color display data to generate kiln temperature monitoring result display data; The kiln monitoring result display module combines the real temperature data of the kiln monitoring area and the kiln monitoring result display data to construct the kiln temperature monitoring result data, and pushes it to the industrial kiln temperature monitoring platform through the Internet of Things communication network, and displays it on the display screen.

[0020] (III) Beneficial effects 1. The present invention matches the real-time air environment characteristic data of the industrial plant area with the air environment impact data of the temperature monitoring, generates the temperature monitoring impact data of the industrial kiln, and performs real temperature data measurement processing with the collected temperature data of the industrial kiln monitoring area to generate the real temperature data of the industrial kiln monitoring area. The real-time operation of the industrial kiln is analyzed according to the real temperature data of the kiln monitoring area, the color display data of the industrial kiln monitoring area is generated, and the kiln temperature monitoring result display data is constructed. Finally, the kiln temperature monitoring result data is constructed and pushed to the industrial kiln temperature monitoring platform, so as to realize the non-contact precise temperature measurement of the industrial kiln, and at the same time, the real-time temperature data of the industrial kiln is intelligently monitored; 2. The Pelican optimization algorithm is used to match the real-time air environment characteristic data of the industrial plant with the temperature monitoring air environment impact data, and accurately match the industrial kiln temperature monitoring impact data that matches the current situation, providing a reliable data basis for the subsequent calculation of the real temperature data. At the same time, the Pelican optimization algorithm can improve the speed and efficiency of the matching process and reduce the response time for obtaining temperature measurement results; 3. The real temperature data calculation formula is used to perform real temperature data measurement processing on the kiln temperature monitoring influence data and the kiln monitoring area temperature data, so as to achieve accurate correction of the collected real-time temperature data, effectively reduce the influence of the air environment and the temperature measurement distance on the non-contact temperature measurement of the industrial kiln, and ensure the accuracy and reliability of the acquired real temperature data; 4. By counting the number of abnormal temperatures in each sub-area within a single cycle and calculating the abnormal temperature ratio, the abnormal temperature ratio is numerically compared with the set threshold value, the real-time temperature conditions of each sub-area are analyzed, and the corresponding color display data is set according to different real-time temperature conditions. The real-time temperature characteristics are highlighted by different colors, and the real-time monitoring results of each sub-area are intuitively displayed, realizing the visualization of the non-contact temperature monitoring results of the industrial kiln. At the same time, the real-time temperature conditions of each sub-area are analyzed through the collected multiple sets of data to ensure the comprehensiveness and accuracy of the temperature analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For ordinary technicians in this field, they can also obtain drawings based on these drawings without paying creative work.

[0022] Figure 1 A flowchart of an artificial intelligence-based non-contact temperature monitoring method for an industrial furnace provided by the present invention; Figure 2 A module schematic diagram of an artificial intelligence-based non-contact temperature monitoring system for industrial furnaces provided by the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] In the description of the present invention, it is necessary to understand that the terms "opening", "upper", "lower", "top", "middle", "inside" and the like indicating orientation or positional relationship are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the components or elements referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the invention.

[0025] Embodiment 1 is as follows: See also Figure 1 , a non-contact temperature monitoring method for industrial kilns based on artificial intelligence, comprising the following steps: S1. Collect real-time air environment characteristic data of industrial plants; S11. The air environment characteristic data in the working area of ​​the target industrial kiln are collected in real time through an air environment monitoring device to obtain real-time air environment characteristic data A of the industrial plant area. The air environment monitoring device includes a temperature sensor, a humidity sensor, a meteorological sensor, a particle sensor and a gas sensor. The air environment characteristic data includes but is not limited to gas composition data, total suspended particulate matter concentration, air temperature, air relative humidity, wind direction data and wind speed data.

[0026] S2. Establish temperature monitoring air environment impact data, perform air environment feature data matching processing on the real-time air environment feature data of the industrial plant area and the temperature monitoring air environment impact data, and generate industrial kiln temperature monitoring impact data; S21. Establish temperature monitoring air environment impact data set B={b1,b2,…,b i ,…,b k}, where b i represents the temperature monitoring influence coefficient corresponding to the i-th type of air environment characteristic data, and k represents the total number of temperature monitoring air environment influence data; S22, using the Pelican optimization algorithm, the real-time air environment characteristic data A of the industrial plant area is matched with the temperature monitoring air environment impact data in the temperature monitoring air environment impact data set to generate an industrial kiln temperature monitoring impact data set. ; S221. Construct an air environment feature search for a pelican population, set the air environment feature search for a pelican population size to N, the current number of iterations to t, and the maximum number of iterations to t. max And the search space dimension of temperature monitoring air environment impact data is P; The temperature monitoring air environment impact data set is used as the temperature monitoring air environment impact data search space, and N temperature monitoring air environment impact data are randomly generated in the temperature monitoring air environment impact data search space, each temperature monitoring air environment impact data corresponds to an air environment feature search pelican individual in the air environment feature search pelican population, and the initial position set of the air environment feature search pelican population is initialized as X={X1,X2,…,X i ,…,X N}, where X i represents the initial position of the i-th air environment feature search pelican individual in the air environment feature search pelican population; S222, calculate the fitness function value of each individual pelican in the pelican population, arrange the pelican individuals in the pelican population from large to small according to the fitness function value, and select the pelican individual with the highest fitness function value as the current optimal individual; the fitness function formula is as follows: , Among them, Fit i represents the fitness function value of the pelican individual searching for the i-th air environment feature, x i represents the deviation value between the air environment characteristic data corresponding to the temperature monitoring air environment impact data corresponding to the i-th pelican individual and the real-time air environment characteristic data of the industrial plant area, Indicates the correction value; S223, exploration stage, each individual pelican in the pelican population locks the prey position in the temperature monitoring air environment impact data search space, and moves towards the prey position to update the position, and the position of the prey in the temperature monitoring air environment impact data search space is randomly generated; the position update formula is as follows: , in, and X ij They respectively represent the position of the i-th air environment feature search pelican individual in the air environment feature search pelican population after and before the position update in the j-dimensional temperature monitoring air environment impact data search space, α represents a random integer uniformly distributed between [1,2], and Z j represents the prey position locked by the i-th air environment feature search pelican individual in the air environment feature search pelican population in the j-dimensional temperature monitoring air environment impact data search space, rand1 and rand2 represent random numbers uniformly distributed between (0,1), respectively, Fit z represents the fitness function value of the prey; S224, development stage, each individual pelican in the pelican population uses a surface flight strategy to hunt more prey in the temperature monitoring air environment impact data search space to update the position; the position update formula is as follows: , Where R represents a random integer with a value of 0 or 2, and rand2 represents a random number that follows a uniform distribution between (0,1); S225, calculating the fitness function value of each individual air environment feature search pelican in the air environment feature search pelican population after the position is updated, if the fitness function value of the individual air environment feature search pelican after the position is updated is less than the original fitness function value, then the new position is used to replace the original position; otherwise, the original position is retained; Rearranging the air environment characteristic searching pelican individuals in the air environment characteristic searching pelican population from large to small according to the fitness function value, and selecting the air environment characteristic searching pelican individual with the highest fitness function value as the new current optimal individual; S226: Determine whether the current number of iterations t is less than the maximum number of iterations t max , if the current number of iterations t is less than the maximum number of iterations t max , then the current iteration number t is increased by 1, and the process returns to S223; otherwise, the current optimal individual is taken as the global optimal solution, the temperature monitoring air environment impact data corresponding to the global optimal solution is output and the data is marked, and the industrial kiln temperature monitoring impact data is generated. .

[0027] S3, collect temperature data of the kiln monitoring area; S31, setting a temperature data collection period, and evenly dividing the temperature data collection period into a number of temperature data collection time points, to obtain a temperature data collection time point set C = {c1, c2, ..., c i ,…,c l}, where c i represents the i-th temperature data collection time point obtained by evenly dividing the temperature data collection period, and l represents the total number of temperature data collection time points; S32, evenly divide the industrial kiln temperature monitoring area into several industrial kiln temperature monitoring sub-areas, and obtain the industrial kiln temperature monitoring sub-area set D = {d1, d2, ..., d i ,…,d p}, where d i represents the i-th industrial kiln temperature monitoring sub-area obtained by evenly dividing the industrial kiln temperature monitoring area, and p represents the total number of industrial kiln temperature monitoring sub-areas; S33, allocating an infrared temperature measuring device to each industrial kiln temperature monitoring sub-area in the kiln temperature monitoring sub-area set, and using the infrared temperature measuring device to collect temperature data of the center position of the corresponding industrial kiln temperature monitoring sub-area in real time at each temperature data collection time point in the temperature data collection time point set, and obtaining the industrial kiln monitoring area temperature data matrix E as follows: , Among them, e ijIt represents the temperature data obtained by real-time collection of the center position of the j-th industrial kiln temperature monitoring sub-area at the i-th temperature data collection time point by the infrared temperature measuring device.

[0028] S4, performing real temperature data measurement processing according to the kiln temperature monitoring influence data and the kiln monitoring area temperature data to generate the real temperature data of the industrial kiln monitoring area; S41, the furnace temperature monitoring influence data is calculated according to the real temperature data calculation formula The real temperature data measurement processing is performed on the industrial kiln monitoring area temperature data in the kiln monitoring area temperature data matrix E to generate the industrial kiln monitoring area real temperature data matrix as follows: , in, It represents the real temperature data of the industrial kiln monitoring area obtained by real-time temperature data measurement processing of the temperature data collected by the infrared temperature measuring device at the center of the jth industrial kiln temperature monitoring sub-area at the i-th temperature data collection time point; the real temperature data calculation formula is as follows: , Among them, D i It represents the distance between the center position of the temperature monitoring sub-area of ​​the i-th industrial kiln and its corresponding infrared temperature measuring device, and D0 represents the unit distance.

[0029] S5. Analyze the real-time operation status of the industrial kiln based on the real temperature data of the kiln monitoring area, and generate color display data of the industrial kiln monitoring area according to the analysis results; S51, set the real temperature threshold, and transform the real temperature data matrix of the kiln monitoring area into The real temperature data of each industrial kiln monitoring area in the temperature monitoring sub-area are numerically compared with the real temperature threshold, and the total number of industrial kiln monitoring area real temperature data corresponding to each industrial kiln temperature monitoring sub-area greater than the real temperature threshold is counted to obtain the industrial kiln monitoring area abnormal temperature number set F={f1,f2,…,f i ,…,f p}, where f i Indicates the total number of real temperature data obtained by collecting temperature data for the i-th industrial furnace temperature monitoring sub-area that are greater than the real temperature threshold; S52, based on the abnormal temperature number of the industrial kiln monitoring area in the kiln monitoring area abnormal temperature number set, calculate the abnormal temperature ratio of each industrial kiln temperature monitoring sub-area, and obtain the industrial kiln monitoring area abnormal temperature ratio set. ,in, represents the abnormal temperature ratio of the temperature monitoring sub-area of ​​the i-th industrial kiln, and ; S53, setting the first threshold value as η1 and the second threshold value as η2, comparing the abnormal temperature ratios of each industrial kiln monitoring area in the kiln monitoring area abnormal temperature ratio set with the first threshold value η1 and the second threshold value η2 in turn, and generating an industrial kiln monitoring area color display data set according to the numerical comparison results. ,in, Indicates the color display data of the industrial kiln monitoring area corresponding to the i-th industrial kiln temperature monitoring sub-area; Generating the industrial furnace monitoring area color display data set according to the numerical comparison result comprises the following steps: S531, selecting the abnormal temperature ratio of the i-th industrial furnace monitoring area and comparing it with the first threshold η1 and the second threshold η2; like , it means that the i-th industrial kiln temperature monitoring sub-area is in a non-working state, and the color display data of the industrial kiln monitoring area corresponding to the i-th industrial kiln temperature monitoring sub-area is output as black; like , it means that the ith industrial kiln temperature monitoring sub-area is in a low temperature state, and the color display data of the industrial kiln monitoring area corresponding to the ith industrial kiln temperature monitoring sub-area is output as yellow; like , it means that the ith industrial kiln temperature monitoring sub-area is at room temperature, and the color display data of the industrial kiln monitoring area corresponding to the ith industrial kiln temperature monitoring sub-area is output as orange; like , it means that the temperature monitoring sub-area of ​​the i-th industrial kiln is in a high temperature state, and the color display data of the industrial kiln monitoring area corresponding to the temperature monitoring sub-area of ​​the i-th industrial kiln is output as red; S532, repeating the operation in S531 until all the abnormal temperature ratios of the industrial kiln monitoring areas in the kiln monitoring area abnormal temperature ratio set are traversed, and a color display data set of the industrial kiln monitoring area is generated.

[0030] S6. Constructing kiln temperature monitoring result display data according to the kiln monitoring area color display data; S61. Construct graphic feature data of the industrial kiln monitoring area, fill the graphic feature data of the kiln monitoring area with color according to the corresponding industrial kiln temperature monitoring sub-area position using the industrial kiln monitoring area color display data in the kiln monitoring area color display data set, and generate kiln temperature monitoring result display data Q.

[0031] S7. constructing kiln temperature monitoring result data based on the real temperature data of the kiln monitoring area and the kiln monitoring result display data, and pushing it to the industrial kiln temperature monitoring platform; S71, the real temperature data matrix of the kiln monitoring area Combine the data with the kiln monitoring result display data Q to construct the kiln temperature monitoring result data ; S72, transmitting the kiln temperature monitoring result data through the Internet of Things communication network Push to the industrial kiln temperature monitoring platform and display on the display screen.

[0032] Embodiment 2 is as follows: See also Figure 2 , an artificial intelligence-based non-contact temperature monitoring system for industrial kilns, comprising an industrial plant area real-time air environment characteristic data acquisition module, an industrial kiln temperature monitoring influence data matching module, an industrial kiln monitoring area temperature data acquisition module, an industrial kiln monitoring area real temperature data metering module, an industrial kiln real-time operation status analysis module, a kiln temperature monitoring result display data construction module, and a kiln temperature monitoring result display module; The industrial plant area real-time air environment characteristic data acquisition module collects the air environment characteristic data in the target industrial kiln working area in real time through the air environment monitoring device to obtain the real-time air environment characteristic data of the industrial plant area; The kiln temperature monitoring impact data matching module performs air environment feature data matching processing on the real-time air environment feature data of the industrial plant area and the established temperature monitoring air environment impact data through the Pelican optimization algorithm to generate industrial kiln temperature monitoring impact data; The kiln monitoring area temperature data acquisition module collects the temperature data of the center position of the corresponding industrial kiln temperature monitoring sub-area in real time at each temperature data acquisition time point through an infrared temperature measuring device to obtain the temperature data of the industrial kiln monitoring area; The real temperature data metering module of the kiln monitoring area performs real temperature data metering processing on the kiln temperature monitoring influence data and the kiln monitoring area temperature data according to the real temperature data calculation formula to generate the real temperature data of the industrial kiln monitoring area; The real-time operation analysis module of the kiln compares the real temperature data of the kiln monitoring area with the real temperature threshold value, counts the total number of real temperature data of the kiln monitoring area that are greater than the real temperature threshold value, calculates the abnormal temperature ratio of the industrial kiln monitoring area, compares the abnormal temperature ratio of the kiln monitoring area with the set first threshold value and second threshold value, and generates color display data of the industrial kiln monitoring area according to the comparison result; The kiln monitoring result display data construction module performs color filling on the constructed industrial kiln monitoring area graphic feature data based on the kiln monitoring area color display data to generate kiln temperature monitoring result display data; The kiln monitoring result display module combines the real temperature data of the kiln monitoring area and the kiln monitoring result display data to construct the kiln temperature monitoring result data, and pushes it to the industrial kiln temperature monitoring platform through the Internet of Things communication network, and displays it on the display screen.

[0033] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0034] The preferred embodiments of the invention disclosed above are only used to help explain the invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the invention, so that those skilled in the art can understand and use the invention well.

Claims

1. A non-contact temperature monitoring method for industrial furnaces based on artificial intelligence, characterized in that: The steps include: S1. Collect real-time air environment characteristic data of industrial plants; S2. Establish temperature monitoring air environment impact data, perform air environment feature data matching processing on the real-time air environment feature data of the industrial plant area and the temperature monitoring air environment impact data, and generate industrial kiln temperature monitoring impact data; S3, collect temperature data of the kiln monitoring area; S4, performing real temperature data measurement processing according to the kiln temperature monitoring influence data and the kiln monitoring area temperature data to generate the real temperature data of the industrial kiln monitoring area; S5. Analyze the real-time operation status of the industrial kiln based on the real temperature data of the kiln monitoring area, and generate color display data of the industrial kiln monitoring area according to the analysis results; S6. Constructing kiln temperature monitoring result display data according to the kiln monitoring area color display data; S7. Construct kiln temperature monitoring result data based on the real temperature data of the kiln monitoring area and the kiln monitoring result display data, and push it to the industrial kiln temperature monitoring platform.

2. According to claim 1, a non-contact temperature monitoring method for industrial furnaces based on artificial intelligence is characterized in that: The S1 comprises the following steps: S11. The air environment characteristic data in the working area of ​​the target industrial kiln is collected in real time through the air environment monitoring device to obtain the real-time air environment characteristic data A of the industrial plant area.

3. The method for non-contact temperature monitoring of industrial furnaces based on artificial intelligence according to claim 2 is characterized in that: The S2 comprises the following steps: S21. Establish temperature monitoring air environment impact data set B={b1,b2,…,b i ,…,b k }, where b i represents the temperature monitoring influence coefficient corresponding to the i-th type of air environment characteristic data, and k represents the total number of temperature monitoring air environment influence data; S22, using the Pelican optimization algorithm to perform air environment feature data matching processing on the temperature monitoring air environment impact data in the temperature monitoring air environment impact data set, and generate industrial kiln temperature monitoring impact data .

4. The method for non-contact temperature monitoring of industrial furnaces based on artificial intelligence according to claim 3 is characterized in that: The S22 comprises the following steps: S221. Construct an air environment feature search for a pelican population, set the air environment feature search for a pelican population size to N, the current number of iterations to t, and the maximum number of iterations to t. max And the search space dimension of temperature monitoring air environment impact data is P; The temperature monitoring air environment impact data set is used as a temperature monitoring air environment impact data search space, and N temperature monitoring air environment impact data are randomly generated in the temperature monitoring air environment impact data search space, each temperature monitoring air environment impact data corresponds to an air environment feature to search for a pelican individual; S222, calculating the fitness function value of each air environment characteristic to search for the pelican individual; S223, in the exploration stage, each pelican individual searches for air environment characteristics to lock the position of the prey in the temperature monitoring air environment impact data search space, and moves towards the prey position to update the position, wherein the position of the prey in the temperature monitoring air environment impact data search space is randomly generated; S224, in the development stage, each air environment feature searches for pelicans that adopt a surface flight strategy to hunt more prey in the temperature monitoring air environment impact data search space to update their positions; S225, calculating the fitness function value of each air environment feature search pelican individual after the position is updated, if the fitness function value of the air environment feature search pelican individual after the position is updated is less than the original fitness function value, then the new position is used to replace the original position; otherwise, the original position is retained; Rearrange the pelican individuals according to the fitness function value from large to small, and select the pelican individual with the highest fitness function value as the new current optimal individual; S226, determine whether t is less than t max , if t is less than t max , then t is increased by 1, and the process returns to S223; otherwise, the current optimal individual is taken as the global optimal solution, the temperature monitoring air environment impact data corresponding to the global optimal solution is output and the data is marked, and the industrial kiln temperature monitoring impact data is generated. .

5. The method for non-contact temperature monitoring of industrial furnaces based on artificial intelligence according to claim 4 is characterized in that: The S3 comprises the following steps: S31, setting a temperature data collection period, and evenly dividing the temperature data collection period into a number of temperature data collection time points, to obtain a temperature data collection time point set C = {c1, c2, ..., c i ,…,c l }, where c i represents the i-th temperature data collection time point obtained by evenly dividing the temperature data collection period, and l represents the total number of temperature data collection time points; S32, evenly divide the industrial kiln temperature monitoring area into several industrial kiln temperature monitoring sub-areas, and obtain the industrial kiln temperature monitoring sub-area set D = {d1, d2, ..., d i ,…,d p }, where d i represents the i-th industrial kiln temperature monitoring sub-area obtained by evenly dividing the industrial kiln temperature monitoring area, and p represents the total number of industrial kiln temperature monitoring sub-areas; S33. Allocate an infrared temperature measuring device to each industrial kiln temperature monitoring sub-area in the kiln temperature monitoring sub-area set, and use the infrared temperature measuring device to collect the temperature data of the center position of the corresponding industrial kiln temperature monitoring sub-area in real time at each temperature data collection time point in the temperature data collection time point set to obtain the industrial kiln monitoring area temperature data matrix E.

6. The method for non-contact temperature monitoring of industrial furnaces based on artificial intelligence according to claim 5 is characterized in that: The S4 comprises the following steps: S41, calculating the temperature according to the actual temperature data formula The temperature data of the industrial kiln monitoring area in E is processed by real temperature data measurement to generate the real temperature data matrix of the industrial kiln monitoring area .

7. The method for non-contact temperature monitoring of industrial furnaces based on artificial intelligence according to claim 6 is characterized in that: The S5 comprises the following steps: S51, set the real temperature threshold, The real temperature data of each industrial kiln monitoring area in the temperature monitoring sub-area are numerically compared with the real temperature threshold, and the total number of industrial kiln monitoring area real temperature data corresponding to each industrial kiln temperature monitoring sub-area greater than the real temperature threshold is counted to obtain the industrial kiln monitoring area abnormal temperature number set F={f1,f2,…,f i ,…,f p }, where f i Indicates the total number of real temperature data obtained by collecting temperature data for the i-th industrial furnace temperature monitoring sub-area that are greater than the real temperature threshold; S52, based on the abnormal temperature number of the industrial kiln monitoring area in the kiln monitoring area abnormal temperature number set, calculate the abnormal temperature ratio of each industrial kiln temperature monitoring sub-area, and obtain the industrial kiln monitoring area abnormal temperature ratio set. ,in, represents the abnormal temperature ratio of the temperature monitoring sub-area of ​​the i-th industrial kiln, and ; S53, setting the first threshold value as η1 and the second threshold value as η2, comparing the abnormal temperature ratios of each industrial kiln monitoring area in the kiln monitoring area abnormal temperature ratio set with the η1 and the η2 in turn, and generating an industrial kiln monitoring area color display data set according to the numerical comparison results. ,in, Indicates the color display data of the industrial kiln monitoring area corresponding to the i-th industrial kiln temperature monitoring sub-area; Generating the industrial furnace monitoring area color display data set according to the numerical comparison result comprises the following steps: S531, selecting the abnormal temperature ratio of the i-th industrial furnace monitoring area and comparing it with the η1 and the η2; like , it means that the i-th industrial kiln temperature monitoring sub-area is in a non-working state, and the color display data of the industrial kiln monitoring area corresponding to the i-th industrial kiln temperature monitoring sub-area is output as black; like , it means that the ith industrial kiln temperature monitoring sub-area is in a low temperature state, and the color display data of the industrial kiln monitoring area corresponding to the ith industrial kiln temperature monitoring sub-area is output as yellow; like , it means that the ith industrial kiln temperature monitoring sub-area is at room temperature, and the color display data of the industrial kiln monitoring area corresponding to the ith industrial kiln temperature monitoring sub-area is output as orange; like , it means that the temperature monitoring sub-area of ​​the i-th industrial kiln is in a high temperature state, and the color display data of the industrial kiln monitoring area corresponding to the temperature monitoring sub-area of ​​the i-th industrial kiln is output as red; S532, repeating the operation in S531 until all the abnormal temperature ratios of the industrial kiln monitoring areas in the kiln monitoring area abnormal temperature ratio set are traversed, and a color display data set of the industrial kiln monitoring area is generated.

8. The method for non-contact temperature monitoring of industrial furnaces based on artificial intelligence according to claim 7 is characterized in that: The S6 comprises the following steps: S61. Construct graphic feature data of the industrial kiln monitoring area, fill the graphic feature data of the kiln monitoring area with color according to the corresponding industrial kiln temperature monitoring sub-area position using the industrial kiln monitoring area color display data in the kiln monitoring area color display data set, and generate kiln temperature monitoring result display data Q.

9. The method for non-contact temperature monitoring of industrial furnaces based on artificial intelligence according to claim 8 is characterized in that: The S7 comprises the following steps: S71, the Combine the data with Q to construct the kiln temperature monitoring result data ; S72, through the Internet of Things communication network Push to the industrial kiln temperature monitoring platform and display on the display screen.

10. A system for implementing the artificial intelligence-based non-contact temperature monitoring method for an industrial furnace as described in any one of claims 1 to 9.

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