CN116735804A - Intelligent sensor precision monitoring system based on Internet of things - Google Patents
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- 239000002341 toxic gas Substances 0.000 claims abstract description 144
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- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 2
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/007—Arrangements to check the analyser
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0062—General constructional details of gas analysers, e.g. portable test equipment concerning the measuring method, e.g. intermittent, or the display, e.g. digital
- G01N33/0063—General constructional details of gas analysers, e.g. portable test equipment concerning the measuring method, e.g. intermittent, or the display, e.g. digital using a threshold to release an alarm or displaying means
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/12—Alarms for ensuring the safety of persons responsive to undesired emission of substances, e.g. pollution alarms
- G08B21/14—Toxic gas alarms
Abstract
The application discloses an intelligent sensor precision monitoring system based on the Internet of things, which relates to the technical field of intelligent sensors and comprises a data acquisition module, a preliminary analysis module, an analysis set establishment module, a comprehensive analysis module and an early warning module; the data acquisition module acquires data information of the poison gas sensor during operation and comprises detection result feedback information and operation state information, and after acquisition, the detection result feedback information and the operation state information of the poison gas sensor during operation are transmitted to the preliminary analysis module. According to the application, through monitoring the operation condition of the toxic gas sensor, when the possibility that the monitoring precision of the toxic gas sensor is influenced is high, the monitoring device prompts workers in the place monitored by the toxic gas sensor to know in time and informs maintainers to carry out relevant maintenance on the toxic gas sensor in time, so that the situation that the toxic gas leakage in the place monitored by the toxic gas sensor cannot be accurately detected is effectively prevented, and the physical and psychological health of the workers in the place is ensured.
Description
Technical Field
The application relates to the technical field of intelligent sensors, in particular to an intelligent sensor precision monitoring system based on the Internet of things.
Background
The intelligent sensor is an intelligent device integrating the functions of the sensor, the processor and the communication. They are able to sense physical or chemical quantities in the environment and process, analyze and transmit the sensed data. The intelligent sensor combines sensor technology with information technology, so that the sensor has higher-level functions and intelligent capability.
A toxic gas sensor is a sensor for detecting and measuring the concentration of harmful gases in an environment, such as carbon monoxide, carbon dioxide, methane, etc. They play a key role in industrial environments, laboratories, mines and other places where potentially toxic gases leak, ensuring the safety of staff;
the prior art has the following defects: then, when the monitoring accuracy of the poison gas sensor in the prior art in the use process becomes poor, the person cannot know in time, and when the poison gas leakage cannot be accurately detected due to the poison gas leakage in the place monitored by the poison gas sensor, huge physical and psychological injuries are caused to staff in the place where the poison gas leakage occurs, so that the practicability of the poison gas sensor becomes poor.
The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
The application aims to provide an intelligent sensor precision monitoring system based on the Internet of things, which monitors the operation condition of a toxic gas sensor, when the monitoring precision of the toxic gas sensor is greatly influenced, sends out an early warning prompt to prompt the personnel in the place monitored by the toxic gas sensor to know in time and inform the maintenance personnel in time to carry out relevant maintenance on the toxic gas sensor, thereby effectively preventing the situation that the toxic gas leakage cannot be accurately detected in the place monitored by the toxic gas sensor, ensuring the physical and psychological health of the personnel in the place, further improving the practicability of the toxic gas sensor and solving the problems in the background technology.
In order to achieve the above object, the present application provides the following technical solutions: the intelligent sensor precision monitoring system based on the Internet of things comprises a data acquisition module, a preliminary analysis module, an analysis set establishment module, a comprehensive analysis module and an early warning module;
the data acquisition module acquires data information of the poison gas sensor during operation, including detection result feedback information and operation state information, and transmits the detection result feedback information and the operation state information of the poison gas sensor during operation to the preliminary analysis module after acquisition;
the primary analysis module is used for establishing a data analysis model by using the feedback information of the detection result and the running state information when the toxic gas sensor runs, generating an influence evaluation coefficient and transmitting the influence evaluation coefficient to the analysis set establishment module;
the analysis set establishment module establishes a data set for an influence evaluation coefficient generated during the operation of the toxic gas sensor and transmits the data set to the comprehensive analysis module;
the comprehensive analysis module is used for comprehensively analyzing the influence evaluation coefficients in the data set to generate an influence comprehensive index, comprehensively analyzing the influence of the toxic gas sensor through the comprehensive index to generate an influence signal, and transmitting the influence signal to the early warning module.
Preferably, the feedback information of the detection result comprises a linear ratio fluctuation coefficient, and after the acquisition, the data acquisition module calibrates the linear ratio fluctuation coefficient as BD y The running state information comprises a drift coefficient and running voltage abnormal frequency, and after the data acquisition module acquires the drift coefficient and the running voltage abnormal frequency, the drift coefficient and the running voltage abnormal frequency are respectively calibrated to Py y and Pvy 。
Preferably, the logic for obtaining the linear ratio fluctuation coefficient is as follows:
s1, acquiring sensor output values and to-be-detected toxic gas concentration values at different moments in T time, and respectively calibrating the sensor output values and the to-be-detected toxic gas concentration values as V x and NDx X represents the numbers of the output value of the sensor and the concentration value of the toxic gas to be detected, which are acquired in the time T, and x=1, 2, 3, 4, … … and c are positive integers;
s2, passing through V at different moments in T time x and NDx Obtaining the linear ratio of different moments in the T time, and calibrating the linear ratio as Bz x Wire(s)Sex ratio Bz x The obtained expression is: bz x ＝V x /ND x ；
S3, obtaining the linear ratio Bz of different moments in the T time x And calibrating the standard deviation as s, wherein the calculation formula of the standard deviation s is as follows:
，
wherein ,is the linear ratio Bz of different moments in time T x Is obtained as:
s4, passing the linear ratio Bz of different moments in the T time x The standard deviation s of the (1) acquires a linear ratio fluctuation coefficient, and the acquired expression is: BD (BD) y ＝s。
Preferably, the logic for drift coefficient acquisition is as follows:
s1, acquiring an initial output value corrected by a toxic gas sensor, and calibrating the initial output value as V ε ；
S2, acquiring real-time output values of the toxic gas sensor at different moments in the T time, and calibrating the real-time output values of the toxic gas sensor as V ε H, H represents the numbers of the real-time output values of the toxic gas sensors at different moments in the T time, and h=1, 2, 3, 4, … … and H are positive integers;
s3, outputting a value V in real time through poison gas sensors at different moments in T time ε h, obtaining a drift coefficient, wherein the obtained expression is as follows:
preferably, the logic for obtaining the abnormal frequency of the operating voltage is as follows:
s1, setting a gradient range V for the voltage of the poison gas sensor in the optimal operation ℉ min～V ℉ max；
S2, acquiring actual operation voltages of the toxic gas sensor at different moments in the T time, and calibrating the actual operation voltages as V ℉ k, k represents the number of the actual operation voltage of the toxic gas sensor at different moments in time T, k=1, 2, 3, 4, … …, N is a positive integer, and V is taken as ℉ k and gradient range V ℉ min～V ℉ max is compared;
s3, V is ℉ k is not in gradient range V ℉ min～V ℉ The actual operating voltage between max is counted and will not lie in the gradient range V ℉ min～V ℉ V between max ℉ k is marked as V ℉ w, w represents not in the gradient range V ℉ min～V ℉ V between max ℉ Number of k, w=1, 2, 3, 4, … …, Q being a positive integer;
s4, calculating abnormal frequency of the operation voltage, wherein the calculated expression is as follows: pv (Pv) y ＝Q/N。
Preferably, the preliminary analysis module obtains the linear ratio fluctuation coefficient BD y Drift coefficient Py y Operating voltage abnormality frequency Pv y Then, a data analysis model is established to generate an influence evaluation coefficient Y Δ P y The formula according to is:
，
wherein mu 1, mu 2 and mu 3 are respectively the linear ratio fluctuation coefficients BD y Drift coefficient Py y Operating voltage abnormality frequency Pv y And μ 1, μ 2, μ 3 are all greater than 0.
Preferably, the analysis set establishment module establishes a data set for an influence evaluation coefficient generated during operation of the toxic gas sensor, and marks the data set as M, so that M= { Y Δ P y }＝{Y Δ P 1 、Y Δ P 2 、…、Y Δ P E Y represents the number of influencing evaluation coefficients within the data set,v=1, 2, 3, 4, … …, E being a positive integer.
Preferably, after the comprehensive analysis module acquires the data set, comprehensive analysis is performed on the influence evaluation coefficients in the data set to generate an influence comprehensive indexThe logic generated is as follows:
setting an influence evaluation coefficient reference threshold value for the influence evaluation coefficient, calibrating the influence evaluation coefficient reference threshold value as yz, comparing the influence evaluation coefficient in the acquired data set with the influence evaluation coefficient reference threshold value respectively, selecting the influence evaluation coefficient with the influence evaluation coefficient larger than or equal to the influence evaluation coefficient reference threshold value, and calibrating the influence evaluation coefficient with the influence evaluation coefficient larger than or equal to the influence evaluation coefficient reference threshold value as Y ε P g Evaluating the coefficient Y by influence within the data set ε P g And an influence evaluation coefficient reference threshold Y Z Calculating an influence comprehensive index, wherein the calculated expression is:where g represents the number of the influence evaluation coefficient equal to or greater than the influence evaluation coefficient reference threshold in the data set, g=1, 2, 3, 4, … …, u, and u is a positive integer.
Preferably, the influence comprehensive index generated in the data set is compared with the influence comprehensive index reference threshold, if the influence comprehensive index is larger than or equal to the influence comprehensive index reference threshold, a high influence signal is generated through the comprehensive analysis module and transmitted to the early warning module, an early warning prompt is sent out through the early warning module, if the influence comprehensive index is smaller than the influence comprehensive index reference threshold, a low influence signal is generated through the comprehensive analysis module and transmitted to the early warning module, and the early warning prompt is not sent out through the early warning module.
In the technical scheme, the application has the technical effects and advantages that:
according to the application, through monitoring the operation condition of the toxic gas sensor, when the possibility that the monitoring precision of the toxic gas sensor is influenced is high, an early warning prompt is sent out to prompt the staff in the place monitored by the toxic gas sensor to know in time and inform the maintainer of carrying out relevant maintenance on the toxic gas sensor in time, so that the situation that the toxic gas leakage cannot be accurately detected in the place monitored by the toxic gas sensor is effectively prevented, the physical and psychological health of the staff in the place is ensured, and the practicability of the toxic gas sensor is further improved;
according to the application, through comprehensively analyzing a plurality of influence evaluation coefficients generated during operation of the toxic gas sensor, the early warning prompt can be effectively prevented from being sent out when the influence evaluation coefficient is larger than or equal to the influence evaluation coefficient reference threshold value due to accidental occurrence during single threshold value comparison, the monitoring accuracy of the toxic gas sensor is ensured, the trust degree of staff in a monitoring place on early warning is further improved, and the stable and efficient operation of the toxic gas sensor is ensured.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for those skilled in the art.
Fig. 1 is a schematic diagram of a module of an intelligent sensor accuracy monitoring system based on the internet of things.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
The application provides an intelligent sensor precision monitoring system based on the Internet of things, which is shown in fig. 1, and comprises a data acquisition module, a preliminary analysis module, an analysis set establishment module, a comprehensive analysis module and an early warning module;
the data acquisition module acquires data information of the poison gas sensor during operation, including detection result feedback information and operation state information, and transmits the detection result feedback information and the operation state information of the poison gas sensor during operation to the preliminary analysis module after acquisition;
the detection result feedback information comprises a linear ratio fluctuation coefficient, and after the detection result feedback information is acquired, the data acquisition module calibrates the linear ratio fluctuation coefficient as BD y ；
The output value of the sensor and the concentration of the toxic gas to be measured have a linear mathematical relationship, in short, when the concentration of the toxic gas changes, the output value of the sensor also changes according to the same proportion, and when the output value of the toxic gas sensor and the concentration of the toxic gas to be measured have linear failure, the measurement accuracy of the toxic gas sensor is seriously affected, which may include the following aspects:
deviation error: a linear failure can lead to a deviation between the sensor output value and the actual concentration, in other words, the relationship between the sensor output value and the actual concentration is no longer linear, but deviates from an ideal linear relationship, and such deviation errors can lead to inaccuracy of the measurement result, so that the sensor cannot provide an accurate concentration measurement value;
nonlinear response: the linear failure may cause nonlinear response characteristics to appear between the output and the concentration of the sensor, which means that the response of the sensor to different concentration levels is not the same in proportion but an irregular change pattern appears, which causes the sensitivity and response speed of the output of the sensor to change in different concentration ranges, further reducing the accuracy and reliability of measurement;
misjudgment and missing report: due to linear failure, the sensor may not accurately judge or report the correct concentration value within a specific concentration range, for example, when the concentration approaches the saturation point of the sensor, the sensor output may not increase any more, resulting in failure to distinguish between different concentration levels of toxic gases, which may lead to misjudgment and false negatives, and failure to accurately identify and report the presence of high or low concentration toxic gases;
difficult to calibrate: the linear failure can increase the complexity and difficulty of sensor calibration, and the nonlinear relationship between the sensor output and the concentration means that a more complex calibration curve or calibration algorithm may be required in the calibration process to correct errors caused by the linear failure, which can lead to a great reduction in the reliability of the sensor;
therefore, the linear relation between the output value of the toxic gas sensor and the concentration of the toxic gas to be detected is monitored, and the problem of measurement accuracy caused by the linear failure of the toxic gas sensor can be found out in time;
the logic for obtaining the linear ratio fluctuation coefficient is as follows:
s1, acquiring sensor output values and to-be-detected toxic gas concentration values at different moments in T time, and respectively calibrating the sensor output values and the to-be-detected toxic gas concentration values as V x and NDx X represents the numbers of the output value of the sensor and the concentration value of the toxic gas to be detected, which are acquired in the time T, and x=1, 2, 3, 4, … … and c are positive integers;
s2, passing through V at different moments in T time x and NDx Obtaining the linear ratio of different moments in the T time, and calibrating the linear ratio as Bz x Linear ratio Bz x The obtained expression is: bz x ＝V x /ND x ；
S3, obtaining the linear ratio Bz of different moments in the T time x And calibrating the standard deviation as s, wherein the calculation formula of the standard deviation s is as follows:
，
wherein ,is the linear ratio Bz of different moments in time T x Is obtained as:
s4, passing the linear ratio Bz of different moments in the T time x Standard deviation s of (2)Obtaining a linear ratio fluctuation coefficient, wherein the obtained expression is as follows: BD (BD) y ＝s；
From the expression, the smaller the standard deviation s of the linear ratio at different times in T time, namely the linear ratio fluctuation coefficient BD y The smaller the expression value of the sensor is, the smaller the fluctuation of the linear ratio is, the smaller the possibility that the monitoring precision of the toxic gas sensor is affected in the use process is, the larger the standard deviation s of the linear ratio at different moments in T time is, namely the fluctuation coefficient BD of the linear ratio y The larger the expression value of the sensor is, the larger the fluctuation of the linear ratio is, and the greater the possibility that the monitoring precision of the toxic gas sensor is affected in the use process is;
the running state information comprises a drift coefficient and running voltage abnormal frequency, and after the data acquisition module acquires the drift coefficient and the running voltage abnormal frequency, the drift coefficient and the running voltage abnormal frequency are respectively calibrated to Py y and Pvy ；
When the poison gas sensor drifts, the following serious influence can be caused on the detection precision:
deviation increases: the drift causes the output value of the sensor to deviate from the accurate value or the calibration value gradually, so that larger deviation exists between the measurement result of the sensor and the actual toxic gas concentration, and the accuracy and precision of the sensor are reduced due to the increase of the deviation, so that the detection result is unreliable;
false alarms or false judgments: when the sensor drifts, the output value of the sensor may exceed or fall below a preset alarm threshold, which may cause false alarm (reporting that toxic gas exists by mistake) or false judgment (failing to detect the toxic gas actually existing), which is very serious for safety monitoring, because false alarm may be caused by false alarm, and false judgment may cause missing alarm;
cumulative error: if drift is not detected and corrected in time, errors are accumulated gradually, and the difference between a detection result and the actual concentration is larger and larger due to the accumulation of the errors along with the time, so that the overall measurement accuracy is reduced;
reliability is reduced: the drift of the sensor may cause unstable or unreliable operation, the output value of the sensor may change drastically in a short time, or exhibit inconsistent output at the same concentration, and such instability may affect the reliability of the sensor, making it difficult to provide consistent and accurate measurement results;
therefore, the drift condition of the toxic gas sensor is monitored, and the problem of measurement accuracy caused by the drift of the toxic gas sensor can be found in time;
the logic for drift coefficient acquisition is as follows:
s1, acquiring an initial output value corrected by a toxic gas sensor, and calibrating the initial output value as V ε ；
S2, acquiring real-time output values of the toxic gas sensor at different moments in the T time, and calibrating the real-time output values of the toxic gas sensor as V ε H, H represents the numbers of the real-time output values of the toxic gas sensors at different moments in the T time, and h=1, 2, 3, 4, … … and H are positive integers;
it should be noted that, the toxic gas sensor generally has an interface for communicating with an external system, such as an analog output, a digital output, a serial port or a wireless communication interface, through which a real-time output value of the toxic gas sensor can be directly obtained;
s3, outputting a value V in real time through poison gas sensors at different moments in T time ε h, obtaining a drift coefficient, wherein the obtained expression is as follows:
from the expression, the drift coefficient Py of the toxic gas sensor in the T time y The larger the expression value of the sensor is, the greater the possibility that the monitoring precision of the toxic gas sensor is affected in the using process is, and the drift coefficient Py of the toxic gas sensor in the T time is y The smaller the expression value of the sensor is, the less the possibility that the monitoring precision of the toxic gas sensor is affected in the use process is shown;
an abnormal operating voltage of the toxic gas sensor may have the following serious influence on the detection accuracy:
offset increases: the detection accuracy of the poison gas sensor is generally dependent on the stability of the voltage supply, and if the operating voltage is abnormal, the output value of the sensor may deviate continuously, resulting in deviation from an accurate value or a calibration value thereof, which will result in an increase in deviation between the detection result and the actual poison gas concentration, thereby reducing the detection accuracy;
sensitivity variation: the sensitivity of the toxic gas sensor can be changed due to abnormal operating voltage, the sensitivity refers to the response capability of the sensor to the change of the concentration of the target toxic gas, if the sensitivity of the sensor is changed due to abnormal voltage, the detection capability of the sensor can be reduced, and the existence of the toxic gas can not be accurately detected or the concentration of the toxic gas can not be accurately measured;
response time delay: the response time of the sensor refers to the time from sensing the existence of toxic gas to outputting a result reaction, and when the operation voltage is abnormal, the response time of the sensor can be prolonged, which leads to the reduction of the real-time performance of the sensor, and the change of the toxic gas can not be detected in time or an accurate concentration measurement result can not be provided;
reliability is reduced: abnormal operating voltages may cause unstable or unreliable operation of the toxic gas sensor, the output value of the sensor may fluctuate unstably or vary unevenly, and even unpredictable faults may occur, which will reduce the reliability of the sensor, making it difficult to provide consistent and accurate detection results;
therefore, the operation voltage of the toxic gas sensor is monitored, and the problem of measurement accuracy caused by abnormal operation voltage of the toxic gas sensor can be found in time;
the logic for obtaining the abnormal frequency of the operating voltage is as follows:
s1, setting a gradient range V for the voltage of the poison gas sensor in the optimal operation ℉ min～V ℉ max；
It should be noted that, manufacturers of toxic gas sensors typically provide detailed specifications and data books that include operating parameters, electrical characteristics, and recommended operating conditions of the sensor, in which the operating voltage range of the sensor and the recommended optimal voltage range are obtained;
s2, acquiring actual operation voltages of the toxic gas sensor at different moments in the T time, and calibrating the actual operation voltages as V ℉ k, k represents the number of the actual operation voltage of the toxic gas sensor at different moments in time T, k=1, 2, 3, 4, … …, N is a positive integer, and V is taken as ℉ k and gradient range V ℉ min～V ℉ max is compared;
s3, V is ℉ k is not in gradient range V ℉ min～V ℉ The actual operating voltage between max is counted and will not lie in the gradient range V ℉ min～V ℉ V between max ℉ k is marked as V ℉ w, w represents not in the gradient range V ℉ min～V ℉ V between max ℉ Number of k, w=1, 2, 3, 4, … …, Q being a positive integer;
s4, calculating abnormal frequency of the operation voltage, wherein the calculated expression is as follows: pv (Pv) y ＝Q/N；
The expression shows that the larger the expression value of the abnormal frequency of the operation voltage of the toxic gas sensor in the T time is, the larger the possibility that the monitoring precision of the toxic gas sensor in the use process is influenced is, and the smaller the expression value of the abnormal frequency of the operation voltage of the toxic gas sensor in the T time is, the smaller the possibility that the monitoring precision of the toxic gas sensor in the use process is influenced is;
the primary analysis module is used for establishing a data analysis model by using the feedback information of the detection result and the running state information when the toxic gas sensor runs, generating an influence evaluation coefficient and transmitting the influence evaluation coefficient to the analysis set establishment module;
the primary analysis module obtains the linear ratio fluctuation coefficient BD y Drift coefficient Py y Operating voltage abnormality frequency Pv y Then, a data analysis model is established to generate an influence evaluation coefficient Y Δ P y The formula according to is:
wherein mu 1, mu 2 and mu 3 are respectively the linear ratio fluctuation coefficients BD y Drift coefficient Py y Abnormal frequency of operation voltageRate Pv y And μ 1, μ 2, μ 3 are all greater than 0;
as can be seen from the formula, the larger the linear ratio fluctuation coefficient of the toxic gas sensor, the larger the drift coefficient and the larger the abnormal frequency of the operating voltage in the T time, namely the influence evaluation coefficient Y Δ P y The larger the expression value of the gas sensor is, the larger the possibility that the monitoring precision of the gas sensor is affected in the using process is, the smaller the fluctuation coefficient of the linear ratio of the gas sensor is, the smaller the drift coefficient is, the smaller the abnormal frequency of the operating voltage is, namely the influence evaluation coefficient Y is Δ P y The smaller the expression value of the sensor is, the less the possibility that the monitoring precision of the toxic gas sensor is affected in the use process is shown;
the analysis set establishment module establishes a data set for an influence evaluation coefficient generated during the operation of the toxic gas sensor and transmits the data set to the comprehensive analysis module;
the analysis set establishment module establishes a data set for an influence evaluation coefficient generated when the toxic gas sensor runs, and the data set is calibrated to be M, so that M= { Y Δ P y }＝{Y Δ P 1 、Y Δ P 2 、…、Y Δ P E Y represents the number of influence assessment coefficients within the data set, v=1, 2, 3, 4, … …, E being a positive integer;
it should be noted that, the number of influence evaluation coefficients in each data set is not specifically limited, but the number of influence evaluation coefficients in each data set is kept equal;
the comprehensive analysis module is used for comprehensively analyzing the influence evaluation coefficients in the data set to generate an influence comprehensive index, comprehensively analyzing the influence of the toxic gas sensor through the comprehensive index to generate an influence signal, and transmitting the influence signal to the early warning module;
after the comprehensive analysis module acquires the data set, comprehensive analysis is carried out on the influence evaluation coefficients in the data set to generate an influence comprehensive indexGenerated patrolThe editing is as follows:
setting an influence evaluation coefficient reference threshold value for the influence evaluation coefficient, calibrating the influence evaluation coefficient reference threshold value as yz, if the influence evaluation coefficient is larger than or equal to the influence evaluation coefficient reference threshold value, indicating that the monitoring precision of the toxic gas sensor is influenced in the using process is larger, if the influence evaluation coefficient is smaller than the influence evaluation coefficient reference threshold value, indicating that the monitoring precision of the toxic gas sensor is influenced in the using process is smaller, comparing the influence evaluation coefficient in the acquired data set with the influence evaluation coefficient reference threshold value respectively, selecting the influence evaluation coefficient with the influence evaluation coefficient larger than or equal to the influence evaluation coefficient reference threshold value, and calibrating the influence evaluation coefficient with the influence evaluation coefficient larger than or equal to the influence evaluation coefficient reference threshold value as Y ε P g Evaluating the coefficient Y by influence within the data set ε P g And an influence evaluation coefficient reference threshold Y Z Calculating an influence comprehensive index, wherein the calculated expression is:wherein g represents the number of the influence evaluation coefficient which is larger than or equal to the reference threshold value of the influence evaluation coefficient in the data set, and g=1, 2, 3, 4, … … and u are positive integers;
comparing the impact comprehensive index generated in the data set with an impact comprehensive index reference threshold, if the impact comprehensive index is greater than or equal to the impact comprehensive index reference threshold, generating a high impact signal through the comprehensive analysis module, transmitting the signal to the early warning module, sending an early warning prompt through the early warning module, prompting personnel in a place monitored by the toxic gas sensor to know in time, and timely notifying maintenance personnel to carry out relevant maintenance on the toxic gas sensor, thereby effectively preventing the situation that the toxic gas leakage cannot be accurately detected in the place monitored by the toxic gas sensor, ensuring the physical and psychological health of the personnel in the place, further improving the practicability of the toxic gas sensor, and if the impact comprehensive index is less than the impact comprehensive index reference threshold, generating a low impact signal through the comprehensive analysis module, transmitting the signal to the early warning module, and not sending the early warning prompt through the early warning module;
according to the application, through monitoring the operation condition of the toxic gas sensor, when the possibility that the monitoring precision of the toxic gas sensor is influenced is high, an early warning prompt is sent out to prompt the staff in the place monitored by the toxic gas sensor to know in time and inform the maintainer of carrying out relevant maintenance on the toxic gas sensor in time, so that the situation that the toxic gas leakage cannot be accurately detected in the place monitored by the toxic gas sensor is effectively prevented, the physical and psychological health of the staff in the place is ensured, and the practicability of the toxic gas sensor is further improved;
according to the application, through comprehensively analyzing a plurality of influence evaluation coefficients generated during operation of the toxic gas sensor, the early warning prompt can be effectively prevented from being sent out when the influence evaluation coefficient is larger than or equal to the influence evaluation coefficient reference threshold value due to accidental occurrence during single threshold value comparison, the monitoring accuracy of the toxic gas sensor is ensured, the trust degree of staff in a monitoring place on early warning is further improved, and the stable and efficient operation of the toxic gas sensor is ensured.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired or wireless means (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the several embodiments provided by the present application, it should be understood that the disclosed systems and methods may be implemented in other ways. For example, the embodiments described above are merely illustrative, e.g., the division of the elements is merely a logical functional division, and there may be additional divisions in actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. The intelligent sensor precision monitoring system based on the Internet of things is characterized by comprising a data acquisition module, a preliminary analysis module, an analysis set establishment module, a comprehensive analysis module and an early warning module;
the data acquisition module acquires data information of the poison gas sensor during operation, including detection result feedback information and operation state information, and transmits the detection result feedback information and the operation state information of the poison gas sensor during operation to the preliminary analysis module after acquisition;
the primary analysis module is used for establishing a data analysis model by using the feedback information of the detection result and the running state information when the toxic gas sensor runs, generating an influence evaluation coefficient and transmitting the influence evaluation coefficient to the analysis set establishment module;
the analysis set establishment module establishes a data set for an influence evaluation coefficient generated during the operation of the toxic gas sensor and transmits the data set to the comprehensive analysis module;
the comprehensive analysis module is used for comprehensively analyzing the influence evaluation coefficients in the data set to generate an influence comprehensive index, comprehensively analyzing the influence of the toxic gas sensor through the comprehensive index to generate an influence signal, and transmitting the influence signal to the early warning module.
2. The intelligent sensor precision monitoring system based on the internet of things according to claim 1, wherein the detection result feedback information comprises a linear ratio fluctuation coefficient, and the data acquisition module calibrates the linear ratio fluctuation coefficient to BD after acquisition y The running state information comprises a drift coefficient and running voltage abnormal frequency, and after the data acquisition module acquires the drift coefficient and the running voltage abnormal frequency, the drift coefficient and the running voltage abnormal frequency are respectively calibrated to Py y and Pvy 。
3. The intelligent sensor accuracy monitoring system based on the internet of things according to claim 2, wherein the logic for obtaining the linear ratio fluctuation coefficient is as follows:
s1, acquiring sensor output values and to-be-detected toxic gas concentration values at different moments in T time, and respectively calibrating the sensor output values and the to-be-detected toxic gas concentration values as V x and NDx X represents the numbers of the output value of the sensor and the concentration value of the toxic gas to be detected, which are acquired in the T time, and x=1, 2, 3, 4, … … and cC is a positive integer;
s2, passing through V at different moments in T time x and NDx Obtaining the linear ratio of different moments in the T time, and calibrating the linear ratio as Bz x Linear ratio Bz x The obtained expression is: bz x ＝V x /ND x ；
S3, obtaining the linear ratio Bz of different moments in the T time x And calibrating the standard deviation as s, wherein the calculation formula of the standard deviation s is as follows:
，
wherein ,is the linear ratio Bz of different moments in time T x Is obtained as:
s4, passing the linear ratio Bz of different moments in the T time x The standard deviation s of the (1) acquires a linear ratio fluctuation coefficient, and the acquired expression is: BD (BD) y ＝s。
4. The intelligent sensor accuracy monitoring system based on the internet of things according to claim 3, wherein the logic for obtaining the drift coefficient is as follows:
s1, acquiring an initial output value corrected by a toxic gas sensor, and calibrating the initial output value as V ε ；
S2, acquiring real-time output values of the toxic gas sensor at different moments in the T time, and calibrating the real-time output values of the toxic gas sensor as V ε H, H represents the numbers of the real-time output values of the toxic gas sensors at different moments in the T time, and h=1, 2, 3, 4, … … and H are positive integers;
s3, outputting a value V in real time through poison gas sensors at different moments in T time ε h is obtainedTaking a drift coefficient, and obtaining an expression as follows:
5. the intelligent sensor accuracy monitoring system based on the internet of things according to claim 4, wherein the logic for acquiring the abnormal frequency of the operating voltage is as follows:
s1, setting a gradient range V for the voltage of the poison gas sensor in the optimal operation ℉ min～V ℉ max；
S2, acquiring actual operation voltages of the toxic gas sensor at different moments in the T time, and calibrating the actual operation voltages as V ℉ k, k represents the number of the actual operation voltage of the toxic gas sensor at different moments in time T, k=1, 2, 3, 4, … …, N is a positive integer, and V is taken as ℉ k and gradient range V ℉ min～V ℉ max is compared;
s3, V is ℉ k is not in gradient range V ℉ min～V ℉ The actual operating voltage between max is counted and will not lie in the gradient range V ℉ min～V ℉ V between max ℉ k is marked as V ℉ w, w represents not in the gradient range V ℉ min～V ℉ V between max ℉ Number of k, w=1, 2, 3, 4, … …, Q being a positive integer;
s4, calculating abnormal frequency of the operation voltage, wherein the calculated expression is as follows: pv (Pv) y ＝Q/N。
6. The intelligent sensor accuracy monitoring system based on the internet of things according to claim 5, wherein the preliminary analysis module obtains a linear ratio fluctuation coefficient BD y Drift coefficient Py y Operating voltage abnormality frequency Pv y Then, a data analysis model is established to generate an influence evaluation coefficient Y Δ P y 。
7. The intelligent transmission based on the internet of things according to claim 6The sensor precision monitoring system is characterized in that an analysis set establishment module establishes a data set for an influence evaluation coefficient generated during operation of a toxic gas sensor, and the data set is calibrated to be M, so that M= { Y Δ P y }＝{Y Δ P 1 、Y Δ P 2 、…、Y Δ P E Y represents the number of influence assessment coefficients within the data set, v=1, 2, 3, 4, … …, E being a positive integer.
8. The intelligent sensor accuracy monitoring system based on the internet of things according to claim 7, wherein after the comprehensive analysis module obtains the data set, the comprehensive analysis module performs comprehensive analysis on the influence evaluation coefficients in the data set to generate an influence comprehensive indexThe logic generated is as follows:
setting an influence evaluation coefficient reference threshold value for the influence evaluation coefficient, calibrating the influence evaluation coefficient reference threshold value as yz, comparing the influence evaluation coefficient in the acquired data set with the influence evaluation coefficient reference threshold value respectively, selecting the influence evaluation coefficient with the influence evaluation coefficient larger than or equal to the influence evaluation coefficient reference threshold value, and calibrating the influence evaluation coefficient with the influence evaluation coefficient larger than or equal to the influence evaluation coefficient reference threshold value as Y ε P g Evaluating the coefficient Y by influence within the data set ε P g And an influence evaluation coefficient reference threshold Y Z Calculating an influence comprehensive index, wherein the calculated expression is:where g represents the number of the influence evaluation coefficient equal to or greater than the influence evaluation coefficient reference threshold in the data set, g=1, 2, 3, 4, … …, u, and u is a positive integer.
9. The intelligent sensor precision monitoring system based on the internet of things according to claim 8, wherein the influence comprehensive index generated in the data set is compared with the influence comprehensive index reference threshold, if the influence comprehensive index is greater than or equal to the influence comprehensive index reference threshold, a high influence signal is generated through the comprehensive analysis module and transmitted to the early warning module, an early warning prompt is sent out through the early warning module, if the influence comprehensive index is smaller than the influence comprehensive index reference threshold, a low influence signal is generated through the comprehensive analysis module and transmitted to the early warning module, and the early warning prompt is not sent out through the early warning module.
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