Gas calibration and information monitoring method and system based on wireless communication

Abstract

The invention discloses a gas calibration and information monitoring method and system based on wireless communication, and relates to the technical field of intelligent environment monitoring, and the method comprises the following steps: arranging a sensor group in a chemical laboratory to monitor environment data in real time, and constructing a data processing platform to carry out data preprocessing and storage; establishing a gas concentration prediction model according to the random forest regression model, and evaluating the gas calibration precision; carrying out real-time gas concentration calibration by adopting a gas calibration formula, and carrying out qualification evaluation; combining the voltage and resistance of the sensor to predict and analyze the health state of the sensor, performing health assessment and generating a corresponding maintenance instruction; the state of the gas sensor is identified by using a broken line diagram, a thermodynamic diagram and color codes, and an operator is helped to quickly identify a gas concentration change trend and a high-risk area. According to the method, the operation efficiency and the maintenance management level of the gas monitoring system are effectively improved, and the method has great advantages especially in a complex experimental environment.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent environmental monitoring, and in particular to a gas calibration and information monitoring method and system based on wireless communication. Background Art

[0002] With the rapid development of industrialization and urbanization, gas pollution and environmental monitoring issues have gradually attracted widespread attention around the world. Especially in the fields of chemical laboratories, industrial manufacturing and environmental monitoring, accurate measurement and real-time monitoring of gas concentrations have become the key to ensuring safety and health. However, traditional gas monitoring technologies often rely on manual inspections and periodic calibration of equipment, resulting in slow response speeds and difficulty in meeting the requirements for monitoring accuracy and real-time performance in dynamic environments. At the same time, sensor aging, drift and changes in environmental factors also cause large errors in the measurement results of gas concentrations, which poses a threat to the accuracy and safety of the experiment. To this end, more and more studies have begun to focus on how to use wireless communications, big data analysis and intelligent algorithms to improve the accuracy, automation and reliability of gas monitoring, gradually promoting the innovation and application of gas calibration and information monitoring technologies.

[0003] At present, traditional gas calibration and gas sensor health monitoring methods have some shortcomings, especially in the long-term stability and accuracy of gas sensors. Most gas sensors will experience decreased sensitivity and calibration deviations as they are used for a longer period of time, affected by factors such as changes in ambient temperature and humidity, variability in gas composition, and internal chemical reactions. Existing monitoring often lacks real-time gas concentration prediction and calibration mechanisms, resulting in manual inspection and adjustment once gas sensors deviate, increasing the difficulty and cost of operation and maintenance. Especially in high-risk laboratories and industrial environments, traditional methods are not easy to effectively monitor and warn the health status of sensors in real time, leading to potential safety hazards. In addition, existing methods are often not easy to accurately predict and calibrate changes in gas concentration, resulting in errors in test results and affecting the accuracy and safety of the experiment. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a gas calibration and information monitoring method and system based on wireless communication, which solves the problems in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A gas calibration and information monitoring method based on wireless communication, comprising the following steps:

[0006] S1. Monitor environmental data in real time through the sensor group arranged in the chemical laboratory, build a data processing platform for preprocessing, obtain the environmental status data group, and then build a data repository through the data processing platform to store the environmental status data group;

[0007] S2. Establish a real-time gas concentration prediction model through a random forest regression model to predict the trend of gas concentration changes, obtain the standard gas concentration, and perform a difference with the real-time collected gas concentration to obtain a deviation index, and perform gas calibration evaluation on the deviation index and the preset accuracy deviation threshold;

[0008] S3, construct a gas calibration formula, and input the environmental state data group into the gas calibration formula, obtain the calibrated gas concentration, and then perform difference processing with the standard gas concentration to obtain the gas concentration qualification, and perform calibration qualification evaluation with the preset accuracy deviation threshold;

[0009] S4, predicting the voltage and resistance of the gas sensor, calculating the comprehensive health assessment coefficient in combination with the environmental status data group, and performing a health assessment of the gas sensor with a preset health threshold;

[0010] S5. Display the changing trend of gas concentration and regional gas concentration level based on line graphs and thermal analysis, and identify the status of gas sensors through color coding.

[0011] Preferably, said S1 includes S11, S12 and S13;

[0012] S11, monitor the environmental data in the chemical laboratory in real time through the sensor groups arranged at different positions in the chemical laboratory, set the sampling frequency to once per second, and mark the collection timestamp;

[0013] The sensor group includes a gas sensor, a temperature sensor, a humidity sensor and a pressure sensor;

[0014] S12, constructing a data processing platform, establishing a communication connection between the data processing platform and the sensor group through a wireless network, transmitting the environmental data to the data processing platform in real time for data correction, missing value processing, denoising and dimensionless processing, and obtaining an environmental state data group;

[0015] The environmental status data group includes gas concentration, indoor temperature, indoor humidity and indoor pressure;

[0016] S13. Build a data repository through the data processing platform, and store the environmental status data group in the data repository in real time for storage.

[0017] Preferably, S2 includes S21, S22 and S23;

[0018] S21. Based on the historical environmental status data group, a nonlinear regression analysis method is used to obtain the nonlinear relationship between the gas concentration nn and the environmental status data group, a real-time gas concentration prediction model is established through a random forest regression model, and the nonlinear relationship between the obtained gas concentration and environmental factors is segmented, trained and verified, the gas concentration prediction model is optimized, and the gas concentration change trend is predicted. The standard gas concentration in the laboratory is predicted based on the gas concentration change trend;

[0019] S22, performing difference processing on the gas concentration that has not been calibrated and the predicted standard gas concentration in the laboratory to obtain a deviation index;

[0020] S23. According to the laboratory monitoring accuracy deviation index requirement of 5%, the accuracy deviation threshold is preset to 5%, and the gas calibration evaluation is performed with the deviation index;

[0021] When the deviation monitoring index is greater than 5%, a first gas calibration instruction is generated to perform gas calibration on the gas sensor;

[0022] When the deviation monitoring index is less than or equal to 5%, gas calibration is not required.

[0023] Preferably, said S3 includes S31, S32 and S33;

[0024] S31. When the gas sensor needs to be calibrated, the gas concentration collected by the gas sensor in real time is used as the basis, and the collected gas concentration is calibrated in real time according to the laboratory environmental status data group. A gas calibration formula is constructed through the data processing platform, and the gas concentration and environmental status data group are input into the gas calibration formula to obtain the calibrated gas concentration.

[0025] Preferably, S32, performing difference processing on the obtained calibrated gas concentration and the predicted standard gas concentration in the laboratory, obtaining the gas concentration qualification, and analyzing the accuracy of the gas sensor;

[0026] S33, performing gas calibration qualification evaluation based on the obtained gas concentration qualification and the preset accuracy deviation threshold, and analyzing the accuracy of the gas sensor. The specific evaluation scheme is as follows;

[0027] When the difference between gas before and after calibration is less than or equal to 5%, the calibration is qualified and the sensor output is accurate;

[0028] When the difference between the gas before and after calibration is greater than 5%, the calibration fails and the sensor health is further analyzed.

[0029] Preferably, S4 includes S41, S42 and S43;

[0030] S41. When the gas calibration qualification assessment is that the calibration is unqualified, the current and voltage of the gas sensor are collected through the electrical interface of the gas sensor, and the resistance of the gas sensor is calculated by Ohm's law. Since the resistance has a nonlinear growth trend with the change of the chemical reaction inside the gas sensor, the aging process and the environmental factors, the resistance is introduced by introducing the aging factor and the temperature correction factor to analyze the resistance change trend of the gas sensor, and the resistance aging model is constructed according to the data processing platform. The environmental factors, the aging factor and the temperature correction factor are introduced into the resistance aging model to predict the sensor resistance.

[0031] S42. Since the change of resistance will directly affect the voltage output of the gas sensor, the change trend of the voltage is obtained according to the change trend of the obtained resistance through Ohm's law.

[0032] Preferably, S43, the health status of the gas sensor needs to be dynamically evaluated based on various parameters obtained in real time, and compared with predetermined standard values ​​for comprehensive evaluation to ensure that the sensor has no aging, drift and failure problems during long-term use, specifically including S431 and S432;

[0033] S431. The health status is comprehensively analyzed based on the voltage, resistance, total working time, indoor temperature and indoor humidity of the gas sensor, taking into account the electrical performance of the gas sensor and the impact of environmental factors on its performance as well as the degradation law of the sensor in long-term use, and building a health analysis model. The obtained voltage, resistance, total working time, indoor temperature and indoor humidity are input into the health analysis model for calculation to obtain a comprehensive health assessment coefficient.

[0034] Preferably, S432, based on the performance indicators and specifications of the gas sensor, a preset gas sensor health threshold is performed, and a gas sensor health assessment is performed with the obtained comprehensive health assessment coefficient, and the aging and damage degree of the gas sensor is comprehensively analyzed, and then relevant instructions are executed according to the assessment results. The specific assessment plan is as follows;

[0035] When the comprehensive health assessment coefficient is greater than the health threshold, the gas sensor is in a healthy state, and a second gas calibration instruction is generated at this time, and the second gas calibration instruction is transmitted to S3 through a wireless network for gas calibration;

[0036] When the comprehensive health assessment coefficient is less than or equal to the health threshold, the gas sensor is in a damaged state. At this time, a replacement instruction is generated and transmitted to relevant personnel through a wireless network to remind them to replace the gas sensor.

[0037] Preferably, the S5 includes S51, S52 and S53;

[0038] S51, extracting the environmental status data group in real time, and displaying the changing trends of the gas concentration and the predicted standard gas concentration in the laboratory through a line graph with two yellow and black lines respectively, wherein the horizontal X-axis of the line graph represents the time axis, which is divided into the dimensions of hours, days, weeks and months, and the vertical Y-axis of the line graph represents the gas concentration;

[0039] S52. According to the gas calibration qualification assessment and the gas sensor health assessment, the status of the gas sensor is identified by color coding, the color coding includes green and red, green indicates that the gas sensor is normal, and red indicates that the gas sensor is faulty and requires emergency treatment;

[0040] S53. Generate heat maps based on gas concentration and location data to display gas concentration levels in areas and gas sensor points, helping staff to quickly locate high-risk areas for key monitoring.

[0041] A gas calibration and information monitoring system based on wireless communication, comprising a data acquisition module, a gas concentration analysis module, a gas calibration module, a health analysis module and a visualization module;

[0042] The data acquisition module monitors environmental data in real time through a sensor group arranged in a chemical laboratory, constructs a data processing platform for preprocessing, obtains an environmental status data group, and then constructs a data repository through the data processing platform to store the environmental status data group;

[0043] The gas concentration analysis module is used to predict the trend of gas concentration changes, obtain the standard gas concentration, and perform a difference with the real-time collected gas concentration to obtain a deviation index, and perform a gas calibration evaluation on the deviation index and the preset accuracy deviation threshold;

[0044] The gas calibration module is used to construct a gas calibration formula, and input the environmental state data group into the gas calibration formula, obtain the calibrated gas concentration, and then perform difference processing with the standard gas concentration to obtain the gas concentration qualification, and perform calibration qualification evaluation with the preset accuracy deviation threshold;

[0045] The health analysis module is used to predict the voltage and resistance of the gas sensor, calculate the comprehensive health assessment coefficient in combination with the environmental status data group, and perform a health assessment of the gas sensor with a preset health threshold;

[0046] The visualization module is used to display the changing trend of gas concentration and regional gas concentration level based on line graphs and thermal analysis, and to identify the status of gas sensors through color coding.

[0047] The present invention provides a gas calibration and information monitoring method and system based on wireless communication, which has the following beneficial effects:

[0048] (1) This method uses a sensor group arranged in the laboratory to continuously monitor environmental data at a sampling frequency of once per second and mark the acquisition timestamp. These data include environmental status data groups such as gas concentration, indoor temperature, humidity and pressure, which are transmitted to the data processing platform through a wireless communication network for real-time correction, missing value processing, denoising and dimensionless processing. The data is stored in a constructed data repository to ensure the integrity and accuracy of real-time data and provide stable and reliable data support. This process ensures the efficiency of subsequent analysis and the stability of the system.

[0049] (2) This method can predict the changing trend of gas concentration by establishing a random forest regression model, and compare the real-time collected gas concentration with the predicted standard gas concentration to obtain a deviation index. This deviation index is used for calibration evaluation. When the deviation index exceeds the preset accuracy threshold, a calibration instruction is generated to automatically start the calibration process of the gas sensor. This mechanism ensures the high accuracy of the gas sensor, avoids errors caused by sensor performance degradation, and maintains the high accuracy of laboratory environmental monitoring. Further calibration qualification assessment will confirm the sensor accuracy. When the calibration error is less than 5%, the sensor output is considered accurate, ensuring the validity of the gas concentration data.

[0050] (3) This method can accurately predict the resistance change of the sensor and evaluate the health status of the sensor through Ohm's law and the resistance aging model. When the comprehensive health assessment coefficient is lower than the set health threshold, maintenance and replacement instructions are issued to ensure the long-term stability and reliability of the gas sensor. The visualization module displays the trend of gas concentration changes through a line graph to help staff intuitively understand the real-time changes in gas concentration in the laboratory, and further displays the distribution of gas concentration and the working status of the sensor through heat maps and color coding. The color coding method can timely reflect the health status of the sensor. Green indicates normal, and red indicates failure and the need for emergency treatment. It provides laboratory staff with a clear and effective monitoring tool to help them quickly locate potential risk areas and implement targeted operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A schematic diagram of the steps of a gas calibration and information monitoring method based on wireless communication according to the present invention;

[0052] Figure 2 The present invention is a flow chart of a gas calibration and information monitoring system based on wireless communication. DETAILED DESCRIPTION

[0053] 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.

[0054] Example 1

[0055] See also Figure 1 The present invention provides a gas calibration and information monitoring method based on wireless communication. To achieve the above purpose, the present invention is implemented through the following technical scheme: comprising the following steps:

[0056] S1. Monitor environmental data in real time through the sensor group arranged in the chemical laboratory, build a data processing platform for preprocessing, obtain the environmental status data group, and then build a data repository through the data processing platform to store the environmental status data group;

[0057] S2. A real-time gas concentration prediction model is established through a random forest regression model to predict the trend of gas concentration changes, obtain the standard gas concentration bzq(t), and perform a difference with the real-time collected gas concentration nn to obtain a deviation index Δnn. The deviation index Δnn is compared with a preset accuracy deviation threshold for gas calibration evaluation;

[0058] S3. When the gas sensor needs to be calibrated, a gas calibration formula is constructed, and the environmental state data group is input into the gas calibration formula to obtain the gas concentration qtn(t) at time t after calibration, and then the difference is processed with the standard gas concentration bzq(t) to obtain the gas concentration qualification Δqtn, and the calibration qualification is evaluated with the preset accuracy deviation threshold;

[0059] S4. When the gas calibration qualification assessment is unqualified, the voltage V(t) and resistance R(t) of the gas sensor are predicted, and the comprehensive health assessment coefficient jkp is calculated in combination with the environmental status data group, and the gas sensor health assessment is performed with the preset health threshold A;

[0060] S5. Display the changing trend of gas concentration nn and the regional gas concentration nn level based on the line graph and thermal analysis, and identify the status of the gas sensor through color coding.

[0061] In this embodiment, the environmental data is collected in real time by arranging sensors, and the data is stored and preprocessed through the data processing platform, so as to ensure the accuracy and timeliness of the monitoring data. The key to this step is the real-time collection and standardized processing of environmental data, which lays a solid foundation for the subsequent prediction and calibration of gas concentrations. The construction of the data repository ensures the systematic management of data, which is conducive to the reliable archiving of long-term trend analysis and experimental results. By introducing the random forest regression model, the changing trend of gas concentration can be predicted in real time, the standard gas concentration bzq(t) can be obtained, and the difference processing is performed with the gas concentration nn collected in real time, so as to obtain the deviation index Δnn, and then the deviation index Δnn is compared with the preset accuracy deviation threshold for gas calibration evaluation; when the gas sensor needs to be calibrated, a gas calibration formula is constructed, and the environmental state data group is input into the gas calibration formula to obtain the gas concentration qtn(t) at time t after calibration. The advantage of this process is that it can accurately evaluate the calibration accuracy of the gas sensor and timely discover and correct the deviation. Compared with the traditional gas monitoring method, this scheme greatly improves the accuracy and reliability of the gas monitoring system through efficient gas concentration prediction and dynamic calibration technology. Especially in the case of variable environmental variables, it can better adapt to the gas concentration monitoring needs under different environmental conditions, reducing the errors caused by equipment aging or environmental changes in traditional methods. Through the health assessment and status monitoring functions, real-time health status assessment and trend visualization of gas sensors are provided. By calculating the sensor voltage V, resistance R and comprehensive health assessment coefficient jkp, timely warning and repair or replacement can be carried out when the sensor fails or performance deteriorates, ensuring the long-term stable operation of the system. Through the visualization of line graphs and heat maps, not only can the changes in gas concentration be intuitively understood, but also the status of the sensor can be grasped in real time, providing data support for maintenance and optimization. Compared with existing technologies, the solution not only improves the accuracy of gas monitoring, but also significantly improves the reliability and maintenance efficiency of the system through the integrated health assessment mechanism, achieving multi-dimensional optimization of gas monitoring accuracy, sensor performance and system operation.

[0062] Example 2

[0063] This embodiment is explained in Example 1, please refer to Figure 1 , specifically: the S1 includes S11, S12 and S13;

[0064] S11, monitor the environmental data in the chemical laboratory in real time through the sensor groups arranged at different positions in the chemical laboratory, set the sampling frequency to once per second, and mark the collection timestamp;

[0065] The sensor group includes a gas sensor, a temperature sensor, a humidity sensor and a pressure sensor;

[0066] S12, constructing a data processing platform, establishing a communication connection between the data processing platform and the sensor group through a wireless network, transmitting the environmental data to the data processing platform in real time for data correction, missing value processing, denoising and dimensionless processing, and obtaining an environmental state data group;

[0067] The environmental state data group includes gas concentration nn, indoor temperature wd, indoor humidity sd and indoor pressure qy;

[0068] S13. Build a data repository through the data processing platform, and store the environmental status data group in the data repository in real time for storage.

[0069] In this embodiment, first, multi-dimensional environmental data is collected by sensor groups arranged at different locations in the laboratory, and the high accuracy and timeliness of data collection are ensured by sampling frequency and timestamp marking once per second. Secondly, with the help of the powerful function of the data processing platform, the collected raw data can be corrected, missing value processed, denoised and dimensionless processed in real time to obtain the environmental state data group, ensuring the accuracy and reliability of the data, thereby providing a stable data basis for subsequent analysis and prediction. Finally, by building a data repository, all environmental state data can be stored and efficiently managed in real time, ensuring the integrity and security of the data. Overall, the implementation of this solution not only improves the collection accuracy and processing efficiency of environmental data, but also provides solid data support for subsequent operations such as gas concentration prediction and sensor calibration through systematic storage and management, ensuring the efficient and stable operation of the monitoring system in a complex environment. The advantage of this process is that it can monitor multiple factors of environmental changes in real time and process them intelligently, greatly improving the automation level of laboratory monitoring and the operability of data.

[0070] Example 3

[0071] This embodiment is explained in Example 2. Please refer to Figure 1 Specifically: S2 includes S21, S22 and S23;

[0072] S21. Based on the historical environmental status data group, a nonlinear regression analysis method is used to obtain the nonlinear relationship between the gas concentration nn and the environmental status data group, a real-time gas concentration prediction model is established through a random forest regression model, and the nonlinear relationship between the obtained gas concentration and environmental factors is segmented, trained and verified, the gas concentration prediction model is optimized, and the gas concentration change trend is predicted. The standard gas concentration bzq(t) in the laboratory at time t is predicted based on the gas concentration change trend;

[0073] S22, performing difference processing on the gas concentration nn(t) at time t without gas calibration and the predicted standard gas concentration bzq(t) in the laboratory at time t to obtain a deviation index Δnn, the specific formula is as follows, Δnn=|nn(t)-bzq(t)*100%;

[0074] S23. According to the laboratory monitoring accuracy deviation index requirement of 5%, the accuracy deviation threshold is preset to 5%, and the gas calibration evaluation is performed with the deviation index Δnn;

[0075] When the deviation monitoring index Δnn is greater than 5%, a first gas calibration instruction is generated to perform gas calibration on the gas sensor;

[0076] When the deviation monitoring index Δnn is less than or equal to 5%, gas calibration is not required.

[0077] In this embodiment, a complex nonlinear relationship between environmental state data and gas concentration is established by combining nonlinear regression analysis and random forest regression model, thereby optimizing the accuracy of the gas concentration prediction model. This dynamic modeling method based on historical data and real-time environmental factors can provide more accurate gas concentration prediction under different environmental conditions, and predict the standard gas concentration bzq(t) in the laboratory at time t according to the trend of gas concentration change, avoiding the delayed response to the change of gas concentration nn in the traditional method. By performing difference processing on the gas concentration nn(t) collected at time t in real time and the predicted standard gas concentration bzq(t) in the laboratory at time t, the deviation index Δnn is accurately obtained, which provides a quantitative basis for the calibration of the gas sensor; by setting the accuracy deviation threshold, it is automatically determined whether gas calibration is required, which not only improves the real-time performance of gas monitoring, but also reduces unnecessary calibration operations and reduces system maintenance costs. In short, this modular calibration process makes gas monitoring more intelligent and adaptive, greatly improves the calibration efficiency and the overall stability of the system, and ensures efficient operation in various complex environments.

[0078] Example 4

[0079] This embodiment is explained in Example 3, please refer to Figure 1 , specifically: S3 includes S31, S32 and S33;

[0080] S31. When the gas sensor needs to be calibrated, the gas concentration nn collected by the gas sensor in real time is used as the basis, and the collected gas concentration is calibrated in real time according to the laboratory environment status data group. A gas calibration formula is constructed through the data processing platform, and then the gas concentration nn(t) and the environment status data group are input into the gas calibration formula to obtain the gas concentration qtn(t) at time t after calibration. The specific gas calibration formula is as follows;

[0081]

[0082] In the formula, k represents the influence coefficient of humidity on gas concentration, qy0 represents standard atmospheric pressure, and wd0 represents standard temperature, usually 0°C, which is used as the reference temperature in physical and chemical calculations.

[0083] S32, performing difference processing on the obtained calibrated gas concentration qtn(t) and the predicted laboratory standard gas concentration bzq(t) at time t, obtaining the gas concentration qualification Δqtn, and analyzing the accuracy of the gas sensor. The specific formula is as follows: Δqtn=|qtn(t)-bzq(t)*100%;

[0084] S33, performing gas calibration qualification evaluation based on the obtained gas concentration qualification Δqtn and the preset accuracy deviation threshold, and analyzing the accuracy of the gas sensor. The specific evaluation scheme is as follows;

[0085] When the difference Δqtn before and after gas calibration is less than or equal to 5%, the calibration is qualified and the sensor output is accurate;

[0086] When the difference Δqtn before and after gas calibration is greater than 5%, the calibration fails and the sensor health is further analyzed.

[0087] In this embodiment, the gas concentration is dynamically corrected by using the gas calibration formula based on the real-time collected gas concentration nn(t) and the laboratory environmental status data group, so that the calibration process can take into account the influence of environmental variables, ensuring the scientificity and adaptability of the calibration results. Through this formula, the calibrated gas concentration qtn(t) can more accurately reflect the concentration changes of the gas in the actual environment, thereby providing more accurate monitoring data. By performing difference processing on the calibrated gas concentration qtn(t) and the standard gas concentration bzq(t) in the laboratory, the gas concentration qualification Δqtn obtained can clearly evaluate the accuracy of the sensor and help to find the calibration error in time. This difference analysis not only improves the transparency of the calibration process, but also provides a quantitative evaluation of the sensor performance, which can effectively control the error within an acceptable range. This multi-stage calibration and evaluation process not only improves the accuracy and reliability of gas monitoring, but also optimizes the adaptability of the sensor by accurately controlling the influence of environmental variables such as humidity sd and temperature wd. In addition, through calibration qualification analysis and health assessment, the monitoring data errors caused by sensor failure or performance degradation are further reduced, more efficient maintenance and fault warning are achieved, and the long-term stability and accuracy of the gas monitoring system are improved.

[0088] Example 5

[0089] This embodiment is explained in Example 4. Please refer to Figure 1 , specifically: the S4 includes S41, S42 and S43;

[0090] S41. When the gas calibration qualification assessment is unqualified, the current I and voltage V of the gas sensor are collected through the electrical interface of the gas sensor, and the resistance R of the gas sensor is calculated by Ohm's law. Since the resistance R has a mutual influence with the changes in the chemical reaction inside the gas sensor, the aging process and the environmental factors, the resistance R shows a nonlinear growth trend. The resistance change trend of the gas sensor is analyzed by introducing the aging factor α and the temperature correction factor β. A resistance aging model is constructed based on the data processing platform. The environmental factors, the aging factor and the temperature correction factor are introduced into the resistance aging model to predict the sensor resistance R(t) at time t. The specific formula algorithm is: R(t)=R0*(1+α*e- λ *sj)*(1+β*wd(t)), where R0 represents the standard resistance of the gas sensor, e represents the exponential function, sj represents the total working time of the gas sensor, and λ represents the time attenuation factor, which is related to the material and working conditions of the sensor;

[0091] S42. Since the change of the resistance R will directly affect the voltage V output of the gas sensor, the change trend of the voltage V is obtained according to the change trend of the resistance R obtained through Ohm's law.

[0092] S43. The health status of the gas sensor needs to be dynamically evaluated based on various parameters obtained in real time, and compared with the predetermined standard values ​​for comprehensive evaluation to ensure that the sensor has no aging, drift and failure problems during long-term use, including S431 and S432;

[0093] S431, the health status is comprehensively analyzed based on the voltage V, resistance R, total working time sj, indoor temperature wd and indoor humidity sd of the gas sensor, comprehensively considering the electrical performance of the gas sensor and the impact of environmental factors on its performance and the degradation law of the sensor in long-term use, and constructing a health analysis model, and inputting the obtained voltage V, resistance R, total working time sj, indoor temperature wd and indoor humidity sd into the health analysis model for calculation to obtain the comprehensive health assessment coefficient jkp. The specific algorithm formula is as follows;

[0094]

[0095] Wherein, V0 represents the standard voltage of the gas sensor, R0 represents the standard resistance of the gas sensor, e represents the exponential function, λ represents the time attenuation factor, w1 and w2 represent the sensitivity parameters of the temperature and humidity on the electrical performance of the sensor, Δwd represents the difference between the ambient temperature and the operating temperature of the gas sensor, Δsd represents the difference between the ambient humidity and the operating humidity of the gas sensor, g represents the calibration correction amplitude parameter of the periodic calibration on the sensor performance, jz represents the time period of the periodic calibration of the gas sensor, sin represents the sine function, π represents the pi, and the value is taken to two decimal places.

[0096] S432. Based on the performance indicators and specifications of the gas sensor, a preset gas sensor health threshold A is set, and the gas sensor health assessment is performed with the obtained comprehensive health assessment coefficient jkp. The aging and damage degree of the gas sensor is comprehensively analyzed, and then relevant instructions are executed according to the assessment results. The specific assessment plan is as follows;

[0097] When the comprehensive health assessment coefficient jkp is greater than the health threshold A, the gas sensor is in a healthy state, and a second gas calibration instruction is generated at this time, and the second gas calibration instruction is transmitted to S3 through a wireless network for gas calibration;

[0098] When the comprehensive health assessment coefficient jkp is less than or equal to the health threshold A, the gas sensor is in a damaged state. At this time, a replacement instruction is generated and transmitted to relevant personnel through a wireless network to remind them to replace the gas sensor.

[0099] In this embodiment, by collecting the current I and voltage V of the gas sensor, combined with Ohm's law and the resistance aging model, the resistance R change trend of the gas sensor during long-term use can be accurately predicted. By introducing the aging factor α and the temperature correction factor β, the resistance aging model can effectively deal with the nonlinear resistance R changes caused by factors such as aging, chemical reactions and environmental changes. This process not only optimizes the service life estimation of the gas sensor, but also provides reliable data support for subsequent health assessments. The output of the voltage V is predicted by the change trend of the resistance R, ensuring that the changes in electrical performance over time are reflected in real time, providing a strong guarantee for the accuracy of gas concentration measurement. Through a comprehensive analysis of the voltage V, resistance R, total working time sj, indoor temperature wd and humidity sd, the health assessment coefficient jkp of the gas sensor is constructed, which can effectively capture the signs of sensor aging, drift and failure. In particular, by weighing the effects of the sensitivity parameters w1 and w2 on temperature and humidity, and introducing the periodic calibration correction amplitude parameter g, the accuracy of the model is improved, ensuring the real-time correction of sensor performance due to environmental changes. On this basis, the health assessment coefficient jkp is compared with the preset health threshold A, which can evaluate the health status of the gas sensor in real time and issue replacement instructions in time, avoiding measurement errors caused by sensor failure. This comprehensive health monitoring mechanism not only improves the reliability and accuracy of the sensor, but also greatly reduces the potential risks caused by equipment failure, ensuring the long-term stable operation of the gas monitoring system.

[0100] Example 6

[0101] This embodiment is explained in Example 5. Please refer to Figure 1 Specifically: S5 includes S51, S52 and S53;

[0102] S51, extracting the environmental status data group in real time, and displaying the changing trends of the gas concentration nn and the predicted standard gas concentration bzq(t) in the laboratory through a line graph with two yellow and black lines respectively, wherein the horizontal X-axis of the line graph represents the time axis, which is divided into the dimensions of hours, days, weeks and months, and the vertical Y-axis of the line graph represents the gas concentration nn;

[0103] S52. According to the gas calibration qualification assessment and the gas sensor health assessment, the status of the gas sensor is identified by color coding, the color coding includes green and red, green indicates that the gas sensor is normal, and red indicates that the gas sensor is faulty and requires emergency treatment;

[0104] S53. Generate heat maps based on gas concentration and location data to display gas concentration levels in areas and gas sensor points, helping staff to quickly locate high-risk areas for key monitoring.

[0105] In this embodiment, a line graph is used to clearly display the changing trends of the real-time gas concentration nn and the standard gas concentration bzq(t), and the horizontal axis division of different time dimensions allows users to intuitively track the short-term and long-term changes in gas concentration. This real-time and dynamic display helps to timely identify abnormal fluctuations in gas concentration and provide data support for further processing and decision-making. Through the color coding function, the health status of the gas sensor is accurately identified, green indicates normal, and red indicates a fault. This mechanism enables operators to respond to problems quickly, avoiding the risk of information lag in traditional methods, and greatly improving maintenance efficiency and system reliability. Finally, a heat map is used to combine gas concentration with location data to intuitively display the concentration distribution of different areas and sensor points, helping staff to quickly identify high-risk areas and conduct accurate key monitoring and emergency response. The combination of these visualization methods and intelligent evaluation mechanisms significantly improves the advantages of the gas monitoring system in real-time, accuracy and response speed, especially in complex environments, by optimizing the workflow, reducing the complexity and risk of manual intervention.

[0106] Example 7

[0107] Please refer to Figure 2 , a gas calibration and information monitoring system based on wireless communication, including a data acquisition module, a gas concentration analysis module, a gas calibration module, a health analysis module and a visualization module;

[0108] The data acquisition module monitors environmental data in real time through a sensor group arranged in a chemical laboratory, constructs a data processing platform for preprocessing, obtains an environmental status data group, and then constructs a data repository through the data processing platform to store the environmental status data group;

[0109] The gas concentration analysis module establishes a real-time gas concentration prediction model through a random forest regression model, predicts the gas concentration change trend, obtains the standard gas concentration bzq(t), and obtains the deviation index Δnn by subtracting it from the real-time collected gas concentration nn, and performs gas calibration evaluation on the deviation index Δnn and the preset accuracy deviation threshold;

[0110] The gas calibration module is used to construct a gas calibration formula after the gas sensor needs to be calibrated, and input the environmental state data group into the gas calibration formula to obtain the gas concentration qtn(t) at time t after calibration, and then perform difference processing with the standard gas concentration bzq(t) to obtain the gas concentration qualification Δqtn, and perform calibration qualification evaluation with a preset accuracy deviation threshold;

[0111] The health analysis module is used to predict the voltage V(t) and resistance R(t) of the gas sensor when the gas calibration qualification assessment is unqualified, calculate the comprehensive health assessment coefficient jkp in combination with the environmental status data group, and perform gas sensor health assessment with the preset health threshold A;

[0112] The visualization module is used to display the changing trend of the gas concentration nn and the regional gas concentration nn level based on the line graph and thermal, and to identify the status of the gas sensor through color coding.

[0113] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A gas calibration and information monitoring method based on wireless communication, characterized in that: The following steps are involved: S1. Monitor environmental data in real time through the sensor group arranged in the chemical laboratory, build a data processing platform for preprocessing, obtain the environmental status data group, and then build a data repository through the data processing platform to store the environmental status data group; S2. Establish a real-time gas concentration prediction model through a random forest regression model to predict the trend of gas concentration changes, obtain the standard gas concentration, and perform a difference with the real-time collected gas concentration to obtain a deviation index, and perform gas calibration evaluation on the deviation index and the preset accuracy deviation threshold; S3, construct a gas calibration formula, and input the environmental state data group into the gas calibration formula, obtain the calibrated gas concentration, and then perform difference processing with the standard gas concentration to obtain the gas concentration qualification, and perform calibration qualification evaluation with the preset accuracy deviation threshold; S4, predicting the voltage and resistance of the gas sensor, calculating the comprehensive health assessment coefficient in combination with the environmental status data group, and performing a health assessment of the gas sensor with a preset health threshold; S5. Display the changing trend of gas concentration and regional gas concentration level based on line graphs and thermal analysis, and identify the status of gas sensors through color coding.

2. A gas calibration and information monitoring method based on wireless communication according to claim 1, characterized in that: Said S1 includes S11, S12 and S13; S11, monitor the environmental data in the chemical laboratory in real time through the sensor groups arranged at different positions in the chemical laboratory, set the sampling frequency to once per second, and mark the collection timestamp; The sensor group includes a gas sensor, a temperature sensor, a humidity sensor and a pressure sensor; S12, constructing a data processing platform, establishing a communication connection between the data processing platform and the sensor group through a wireless network, transmitting the environmental data to the data processing platform in real time for data correction, missing value processing, denoising and dimensionless processing, and obtaining an environmental state data group; The environmental status data group includes gas concentration, indoor temperature, indoor humidity and indoor pressure; S13. Build a data repository through the data processing platform, and store the environmental status data group in the data repository in real time for storage.

3. A gas calibration and information monitoring method based on wireless communication according to claim 2, characterized in that: The S2 includes S21, S22 and S23; S21. Based on the historical environmental status data group, a nonlinear regression analysis method is used to obtain the nonlinear relationship between the gas concentration nn and the environmental status data group, a real-time gas concentration prediction model is established through a random forest regression model, and the nonlinear relationship between the obtained gas concentration and environmental factors is segmented, trained and verified, the gas concentration prediction model is optimized, and the gas concentration change trend is predicted. The standard gas concentration in the laboratory is predicted based on the gas concentration change trend; S22, performing difference processing on the gas concentration that has not been calibrated and the predicted standard gas concentration in the laboratory to obtain a deviation index; S23. According to the laboratory monitoring accuracy deviation index requirement of 5%, the accuracy deviation threshold is preset to 5%, and the gas calibration evaluation is performed with the deviation index; When the deviation monitoring index is greater than 5%, a first gas calibration instruction is generated to perform gas calibration on the gas sensor; When the deviation monitoring index is less than or equal to 5%, gas calibration is not required.

4. A gas calibration and information monitoring method based on wireless communication according to claim 3, characterized in that: The S3 includes S31, S32 and S33; S31. When the gas sensor needs to be calibrated, the gas concentration collected by the gas sensor in real time is used as the basis, and the collected gas concentration is calibrated in real time according to the laboratory environmental status data group. A gas calibration formula is constructed through the data processing platform, and the gas concentration and environmental status data group are input into the gas calibration formula to obtain the calibrated gas concentration.

5. A method for gas calibration and information monitoring based on wireless communication according to claim 4, characterized in that: S32, performing difference processing on the obtained calibrated gas concentration and the predicted standard gas concentration in the laboratory, obtaining the gas concentration qualification, and analyzing the accuracy of the gas sensor; S33, performing gas calibration qualification evaluation based on the obtained gas concentration qualification and the preset accuracy deviation threshold, and analyzing the accuracy of the gas sensor. The specific evaluation scheme is as follows; When the difference between gas before and after calibration is less than or equal to 5%, the calibration is qualified and the sensor output is accurate; When the difference between the gas before and after calibration is greater than 5%, the calibration fails and the sensor health is further analyzed.

6. A gas calibration and information monitoring method based on wireless communication according to claim 5, characterized in that: The S4 includes S41, S42 and S43; S41. When the gas calibration qualification assessment is that the calibration is unqualified, the current and voltage of the gas sensor are collected through the electrical interface of the gas sensor, and the resistance of the gas sensor is calculated by Ohm's law. Since the resistance has a nonlinear growth trend with the change of the chemical reaction inside the gas sensor, the aging process and the environmental factors, the resistance is introduced by introducing the aging factor and the temperature correction factor to analyze the resistance change trend of the gas sensor, and the resistance aging model is constructed according to the data processing platform. The environmental factors, the aging factor and the temperature correction factor are introduced into the resistance aging model to predict the sensor resistance. S42. Since the change of resistance will directly affect the voltage output of the gas sensor, the change trend of the voltage is obtained according to the change trend of the obtained resistance through Ohm's law.

7. A method for gas calibration and information monitoring based on wireless communication according to claim 6, characterized in that: S43. The health status of the gas sensor needs to be dynamically evaluated based on various parameters obtained in real time, and compared with the predetermined standard values ​​for comprehensive evaluation to ensure that the sensor has no aging, drift and failure problems during long-term use, including S431 and S432; S431. The health status is comprehensively analyzed based on the voltage, resistance, total working time, indoor temperature and indoor humidity of the gas sensor, taking into account the electrical performance of the gas sensor and the impact of environmental factors on its performance as well as the degradation law of the sensor in long-term use, and building a health analysis model. The obtained voltage, resistance, total working time, indoor temperature and indoor humidity are input into the health analysis model for calculation to obtain a comprehensive health assessment coefficient.

8. A gas calibration and information monitoring method based on wireless communication according to claim 7, characterized in that: S432. Based on the performance indicators and specifications of the gas sensor, a preset gas sensor health threshold is performed, and a gas sensor health assessment is performed with the obtained comprehensive health assessment coefficient, and the aging and damage degree of the gas sensor is comprehensively analyzed. Then, relevant instructions are executed according to the assessment results. The specific assessment plan is as follows; When the comprehensive health assessment coefficient is greater than the health threshold, the gas sensor is in a healthy state, and a second gas calibration instruction is generated at this time, and the second gas calibration instruction is transmitted to S3 through a wireless network for gas calibration; When the comprehensive health assessment coefficient is less than or equal to the health threshold, the gas sensor is in a damaged state. At this time, a replacement instruction is generated and transmitted to relevant personnel through a wireless network to remind them to replace the gas sensor.

9. A gas calibration and information monitoring method based on wireless communication according to claim 1, characterized in that: The S5 includes S51, S52 and S53; S51, extracting the environmental status data group in real time, and displaying the changing trends of the gas concentration and the predicted standard gas concentration in the laboratory through a line graph with two yellow and black lines respectively, wherein the horizontal X-axis of the line graph represents the time axis, which is divided into the dimensions of hours, days, weeks and months, and the vertical Y-axis of the line graph represents the gas concentration; S52. According to the gas calibration qualification assessment and the gas sensor health assessment, the status of the gas sensor is identified by color coding, the color coding includes green and red, green indicates that the gas sensor is normal, and red indicates that the gas sensor is faulty and requires emergency treatment; S53. Generate heat maps based on gas concentration and location data to display gas concentration levels in areas and gas sensor points, helping staff to quickly locate high-risk areas for key monitoring.

10. A gas calibration and information monitoring system based on wireless communication, comprising a gas calibration and information monitoring method based on wireless communication according to any one of claims 1 to 9, characterized in that: It includes data acquisition module, gas concentration analysis module, gas calibration module, health analysis module and visualization module; The data acquisition module monitors environmental data in real time through a sensor group arranged in a chemical laboratory, constructs a data processing platform for preprocessing, obtains an environmental status data group, and then constructs a data repository through the data processing platform to store the environmental status data group; The gas concentration analysis module is used to predict the trend of gas concentration changes, obtain the standard gas concentration, and perform a difference with the real-time collected gas concentration to obtain a deviation index, and perform a gas calibration evaluation on the deviation index and the preset accuracy deviation threshold; The gas calibration module is used to construct a gas calibration formula, and input the environmental state data group into the gas calibration formula, obtain the calibrated gas concentration, and then perform difference processing with the standard gas concentration to obtain the gas concentration qualification, and perform calibration qualification evaluation with the preset accuracy deviation threshold; The health analysis module is used to predict the voltage and resistance of the gas sensor, calculate the comprehensive health assessment coefficient in combination with the environmental status data group, and perform a health assessment of the gas sensor with a preset health threshold; The visualization module is used to display the changing trend of gas concentration and regional gas concentration level based on line graphs and thermal analysis, and to identify the status of gas sensors through color coding.

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