Harmful gas detection alarm method and system

Abstract

The invention relates to the field of harmful gas detection, in particular to a harmful gas detection alarm method and system, and the method comprises the steps: calculating a correction coefficient of a collection value of a sensor, taking the product of the correction coefficient and the collection value as a correction value, and carrying out the abnormal alarm according to the correction value; the calculation method of the correction coefficient comprises the following steps: calculating the influence of air flow on the sensor according to the change of the collection value of the harmful gas concentration to obtain an influence factor; constructing a concentration sequence of sensor acquisition values, and calculating the confidence degree of the sensor according to the concentration sequence; and normalizing the ratio of the confidence degree of the sensor to the influence factor, and taking the normalized ratio as a correction coefficient. The method has the effect of improving the accuracy of the detection result of the harmful gas.

Description

Technical Field

[0001] The present application relates to the field of harmful gas detection, and in particular to a harmful gas detection and alarm method and system. Background Art

[0002] With the acceleration of industrialization, the emission of various harmful gases has become one of the important factors of environmental pollution. Especially in industries such as mining and chemical industry, the leakage of harmful gases not only endangers the health of workers, but also may cause serious accidents such as fire and explosion. For example, in chemical enterprises, common harmful gases include irritating gases such as chlorine, ammonia, and nitrogen oxides, as well as asphyxiating gases such as carbon monoxide and hydrogen sulfide. Once these gases leak, they will not only cause acute poisoning to on-site workers, but may also cause explosions due to gas accumulation, resulting in major casualties and property losses. Therefore, timely detection and alarm of the leakage of harmful gases is of great significance to ensure personnel safety and environmental protection.

[0003] The existing technology can detect whether harmful gas leaks by deploying sensors, such as the Chinese patent with patent publication number CN119229605A, a chemical gas leak detection management method and system, which discloses the deployment of monitoring equipment in the detection area, such as air quality monitoring stations, gas monitors, sensors, etc., and configures the monitoring parameters of the monitoring equipment according to the monitoring target and gas type. These monitoring equipment can monitor and warn enterprises in a timely manner through real-time monitoring and early warning mechanisms.

[0004] In the process of using sensors to detect harmful gases, air flow may change the local distribution of gas concentration. Especially in complex industrial environments, unstable airflow may cause the gas concentration detected by the sensor to be inconsistent with the actual concentration, resulting in false detection of the sensor and inaccurate alarm results. Summary of the invention

[0005] In order to solve the technical problem of inaccurate detection results of harmful gases, the present application provides a detection and alarm method and system for harmful gases.

[0006] In the first aspect, the present application provides a method for detecting and alarming harmful gases, which adopts the following technical solution: A method for detecting and alarming harmful gases comprises the following steps: calculating a correction coefficient of a sensor acquisition value, taking the product of the correction coefficient and the acquisition value as the correction value, and making an abnormal alarm according to the correction value; the correction coefficient is calculated by: calculating the influence of air flow on the sensor according to the change of the acquisition value of the harmful gas concentration to obtain an influence factor; constructing a concentration sequence of the sensor acquisition value, and calculating the confidence level of the sensor according to the concentration sequence; and normalizing the ratio of the confidence level of the sensor to the influence factor as the correction coefficient.

[0007] The beneficial effects are: the influence of air flow on the sensor is quantified as an influence factor and incorporated into the correction coefficient calculation, which can more accurately reflect the gas concentration in the actual detection environment; the confidence level quantifies the reliability of the sensor's data collection under the current environment and working conditions. The correction coefficient is calculated based on the confidence level and the influence factor, and the sensor collection value is corrected by the correction coefficient, which can effectively compensate for the measurement error caused by the sensor's own characteristics or environmental factors, making the detection value closer to the true value, so as to improve the accuracy of the detection results of harmful gases.

[0008] Optionally, the impact factor is calculated as: , where Indicates The influencing factors of each sensor; Indicates The total number of acquisition moments of sensors; Indicates the collection time sequence number; Indicates The sensor at the time The collected value of harmful gas concentration; Indicates The average concentration of harmful gases collected by the sensors at all times; Represents the standard normalization function.

[0009] The beneficial effects are: providing a method for quantifying the effect of air flow on the sensor, It indicates the concentration difference collected by the sensor at different times. The larger the value, the greater the concentration difference, the more unstable the concentration at the location of the sensor, that is, the greater the impact of air flow on the collected value of the sensor. On the contrary, the smaller the impact of air flow on the collected value of the sensor. And this formula directly calculates the absolute difference between the concentration value and the mean at each moment, and then sums and averages them. This calculation method is sensitive to abnormal values, because the absolute value will amplify the difference and can better reflect the concentration fluctuation at the location of the sensor.

[0010] Optionally, the impact factor is calculated as: , where Indicates The influencing factors of each sensor; Indicates The total number of acquisition moments of sensors; Indicates the collection time sequence number; Indicates The sensor at the time The collected value of harmful gas concentration; Indicates The average concentration of harmful gases collected by the sensors at all times; Represents the standard normalization function.

[0011] The beneficial effect is that another method for quantifying the influence of air flow on the sensor is provided, and the calculation method is relatively less sensitive to abnormal values ​​because the square and square root operations smooth the differences and can better reflect the concentration stability at the location of the sensor.

[0012] Optionally, the confidence level calculation formula is: constructing a concentration sequence of sensor acquisition values, including: calculating the Euclidean distance between any sensor and the position of other sensors, sorting the sensors in order of Euclidean distance from large to small or from small to large, taking the sensor acquisition values ​​as elements in the sequence, and constructing a concentration sequence of any sensor acquisition values.

[0013] The beneficial effect is: when using sensors to detect concentration values, in order to prevent a single sensor from being disturbed by the external environment, resulting in errors in the collected data, and then causing early warning errors, therefore, for any sensor, the present application calculates the confidence level of the concentration value of the sensor according to the changes in the concentration values ​​collected by the sensor and its surrounding sensors. Euclidean distance can quantify the spatial position relationship between sensors. By calculating and sorting the Euclidean distance, the spatial correlation of the sensor collected values ​​can be more accurately reflected. In the detection of harmful gases, the collected values ​​of sensors that are closer often have higher similarity and correlation. This data fusion method based on spatial position can more truly reflect the distribution of harmful gases in space.

[0014] Optionally, in calculating the confidence level of the sensor according to the concentration sequence, the confidence level is calculated as follows: , where Indicates The sensor at the time The confidence level; Indicates The total number of data points in the concentration series for each sensor; Indicates the ordinal number of the data point in the concentration series; Indicated by natural constant The exponential function with base ; Indicates The collected value of each sensor is consistent with the concentration sequence. The difference of the collected values; Indicates The mean of the differences between the values ​​collected by each sensor and the concentration series; Indicates The concentration sequence of the sensor A collection value; Indicates The collected values ​​of sensors; Indicates Among the sensors The Euclidean distance corresponding to the collected values.

[0015] The beneficial effects are: By using the ratio of concentration difference to its corresponding distance, the influence caused by excessive Euclidean distance or concentration difference can be avoided. Indicates the closeness of the difference. The closer it is, the less likely it is that the sensor is affected by the external environment and the data collected by the sensor has errors, that is, the higher the confidence level of the sensor at that moment. Conversely, the lower the confidence level.

[0016] Optionally, in calculating the confidence level of the sensor according to the concentration sequence, the confidence level is calculated as follows: , where Indicates The sensor at the time The confidence level; Indicates The total number of data points in the concentration series for each sensor; Indicates the ordinal number of the data point in the concentration series; Indicated by natural constant The exponential function with base ; Indicates The collected value of each sensor is consistent with the concentration sequence. The difference of the collected values; Indicates The mean of the differences between the values ​​collected by each sensor and the concentration series; Indicates The concentration sequence of the sensor A collection value; Indicates The collected values ​​of the sensors.

[0017] The beneficial effect is: it provides another method for calculating the The collected value of each sensor is consistent with the concentration sequence. The method of calculating the difference between the collected values ​​simplifies the calculation of the confidence level.

[0018] Optionally, the product of the correction coefficient and the collected value is used as the correction value, and an abnormal alarm is performed based on the correction value, including the steps of: constructing an isolated tree according to the isolation forest algorithm, and calculating the abnormal score of the correction value according to the position of the correction value in the isolated tree; in response to the abnormal score of the correction value corresponding to all sensors or a preset number of sensors being greater than a preset threshold, generating an alarm signal.

[0019] In the second aspect, the present application provides a harmful gas detection and alarm system, which adopts the following technical solution: A harmful gas detection and alarm system comprises: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the harmful gas detection and alarm method described above is implemented.

[0020] The beneficial effect is: the above-mentioned harmful gas detection and alarm method is generated into a computer program and stored in a memory so as to be loaded and executed by a processor, thereby making a system based on the memory and the processor for easy use.

[0021] This application has the following technical effects: 1. Quantifying the influence of air flow on the sensor as an influence factor and incorporating it into the correction coefficient calculation can more accurately reflect the gas concentration in the actual detection environment; the confidence level quantifies the reliability of the sensor's data collection under the current environment and working conditions. The correction coefficient is calculated based on the confidence level and the influence factor. The sensor collection value is corrected by the correction coefficient, which can effectively compensate for the measurement error caused by the sensor's own characteristics or environmental factors, making the detection value closer to the true value, so as to improve the accuracy of the detection results of harmful gases.

[0022] 2. Taking the ratio of the sensor's confidence level to the influencing factor as the core part of the correction coefficient can comprehensively consider the reliability of the sensor data and the impact of environmental factors on the sensor. Normalizing the ratio of the confidence level to the influencing factor can limit the range of the correction coefficient to a reasonable range to avoid abnormalities in the corrected acquisition values ​​due to excessive or too small ratios. The normalized correction coefficient can ensure the stability and reliability of the correction process, making the corrected acquisition values ​​comparable and consistent, which is convenient for subsequent analysis and processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present application will become easily understood. In the accompanying drawings, several embodiments of the present application are shown in an exemplary and non-restrictive manner, and the same or corresponding numbers represent the same or corresponding parts.

[0024] Figure 1 It is a method flow chart of a harmful gas detection and alarm method in an embodiment of the present application.

[0025] Figure 2 This is a method flow chart of step S1 in a method for detecting and alarming harmful gases in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0027] It should be understood that when the terms "first", "second", etc. are used in the claims, specification and drawings of the present application, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the specification and claims of the present application indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their collections.

[0028] The present application embodiment discloses a method for detecting and alarming harmful gases, referring to Figure 1 , including steps S1-S2, which are as follows: S1: Calculate the correction coefficient of the sensor acquisition value.

[0029] Acquire the collected values ​​of the sensor, evenly arrange multiple sensors in the area that needs to be detected, and obtain harmful gas detection data. Harmful gas detection data refers to the concentration of harmful gases and the time of collection obtained through the detection system. For example, harmful gases can be carbon monoxide, which is colorless and odorless and is not easily detected in working environments such as mines and industrial environments. However, they can cause serious harm to the human body. Long-term exposure to an environment with high carbon monoxide levels can cause headaches, nausea, and even coma. Regularly detecting the concentration of these gases can effectively prevent poisoning incidents.

[0030] In one embodiment, a fixed gas detection device (such as an electrochemical sensor) is used to continuously detect the carbon monoxide concentration in a mine shaft, wherein multiple sensors are arranged in the mine shaft for detection, the interval between the sensors is one meter, and the preset acquisition frequency is 1 Hz.

[0031] At this point, the harmful gas detection data (sensor collection value) is acquired. Then the correction coefficient of the sensor collection value is calculated, and the sensor collection value is corrected according to the correction coefficient.

[0032] Reference Figure 2 , the calculation method of the correction coefficient includes steps S10-S12, which are as follows: S10: According to the change of the collected value of the harmful gas concentration, the influence of the air flow on the sensor is calculated to obtain the influence factor.

[0033] In a complex working environment, the instability of airflow can cause local fluctuations in gas concentration. This is because air flow can cause gas to form gradients of different concentrations in space, especially in areas with uneven ventilation and complex airflow distribution. Sensors can usually only detect the gas concentration at their location, but cannot reflect the gas distribution in a larger range. The installation location of the sensor is in a place with large disturbances, and the gas concentration it detects may be significantly different from the actual concentration in other areas, resulting in inaccurate data. Therefore, this application needs to calculate the factors affecting the air flow on the sensor based on the changes in the sensor's gas concentration.

[0034] In one embodiment, the calculation formula of the impact factor is: , where Indicates The influencing factors of each sensor; Indicates The total number of acquisition moments of sensors; Indicates the collection time sequence number; Indicates The sensor at the time The collected value of harmful gas concentration; Indicates The average concentration of harmful gases collected by the sensors at all times; Represents the standard normalization function.

[0035] in, It indicates the concentration difference collected by the sensor at different times. The larger the value, the greater the concentration difference, the more unstable the concentration at the location of the sensor, that is, the greater the impact of air flow on the collected value of the sensor. Conversely, the smaller the impact of air flow on the collected value of the sensor.

[0036] This formula directly calculates the absolute difference between the concentration value at each moment and the mean, and then sums and averages them. This calculation method is sensitive to outliers because the absolute value will amplify the difference and can better reflect the concentration fluctuation at the location of the sensor.

[0037] In other embodiments, the calculation formula of the impact factor may also be: . This formula calculates the concentration value at each moment With the mean This calculation method is relatively less sensitive to outliers because the square and square root operations smooth out the differences and better reflect the concentration stability at the sensor location.

[0038] S11: Construct a concentration sequence of the sensor acquisition values, and calculate the confidence level of the sensor according to the concentration sequence.

[0039] When using sensors to detect concentration values, in order to prevent a single sensor from being disturbed by the external environment, resulting in errors in the collected data, and then causing warning errors, for any sensor, this application calculates the confidence level of the concentration value of the sensor based on the changes in the concentration values ​​collected by the sensor and the sensors around it. If there is a leak of harmful gas, the concentration of the sensor closer to the leak source is higher and the concentration is closer. As the distance from the leak source increases, its concentration shows a decreasing trend. If the collected data has errors due to external environmental factors, this feature does not exist. Therefore, this application calculates the confidence level of the sensor data by analyzing the gradient changes in the gas concentration values ​​detected by the sensor and the sensors around it at the same time.

[0040] In one embodiment, constructing a concentration sequence of sensor acquisition values ​​includes: calculating the Euclidean distance between any sensor and other sensor positions, sorting the sensors in order of Euclidean distance from large to small or from small to large, taking the sensor acquisition values ​​as elements in the sequence, and constructing a concentration sequence of any sensor acquisition values.

[0041] In one embodiment, the confidence level is calculated as follows: , where Indicates The sensor at the time The confidence level; Indicates The total number of data points in the concentration series for each sensor; Indicates the ordinal number of the data point in the concentration series; Indicated by natural constant The exponential function with base ; Indicates The collected value of each sensor is consistent with the concentration sequence. The difference of the collected values; Indicates The mean of the differences between the values ​​collected by each sensor and the concentration series; Indicates The concentration sequence of the sensor A collection value; Indicates The collected values ​​of sensors; Indicates Among the sensors The Euclidean distance corresponding to the collected values.

[0042] By using the ratio of concentration difference to its corresponding distance, the influence caused by excessive Euclidean distance or concentration difference can be avoided. Indicates the closeness of the difference. The closer it is, the less likely it is that the sensor is affected by the external environment and the data collected by the sensor has errors, that is, the higher the confidence level of the sensor at that moment. Conversely, the lower the confidence level.

[0043] In other embodiments, The calculation formula can also be: , directly using the concentration difference without considering the influence of distance, so it is highly sensitive to outliers. In practical applications, the appropriate formula can be selected according to the specific situation. If the distance difference between sensors is large, using the former confidence level calculation formula can better reflect the confidence level; if the distance difference between sensors is small, using the latter confidence level calculation formula can simplify the calculation.

[0044] S12: The ratio of the confidence level of the sensor to the influencing factor is normalized and used as the correction coefficient.

[0045] The product of the correction coefficient and the collected value is taken as the correction value, and the expression of the correction value can be: , where Indicates Correction value of the sensor acquisition value; Indicates The collected values ​​of sensors; Indicates The confidence level of each sensor; Indicates The influencing factor of the sensor. represents the correction factor, represents the standard normalization function. The average confidence level of each sensor at all times is taken as The confidence level of each sensor is The mean value of .

[0046] Taking the ratio of the sensor's confidence level to the influence factor as the core of the correction coefficient can comprehensively consider the reliability of the sensor data and the impact of environmental factors on the sensor. The collected values ​​of sensors with high confidence levels are more reliable and should be given greater weight; while sensors with large influence factors are more seriously affected by environmental interference and require greater corrections. Through this comprehensive consideration, the collected values ​​of each sensor can be corrected more accurately, making them closer to the actual concentration of harmful gases.

[0047] Normalizing the ratio of confidence level to influencing factor can limit the range of correction coefficient to a reasonable range, avoiding abnormal correction of collected values ​​due to excessive or too small ratio. The normalized correction coefficient can ensure the stability and reliability of the correction process, making the corrected collected values ​​comparable and consistent, which is convenient for subsequent analysis and processing.

[0048] S2: The product of the correction coefficient and the collected value is used as the correction value, and an abnormal alarm is issued according to the correction value.

[0049] An isolation tree is constructed according to the isolation forest algorithm, and an abnormal score of the correction value is calculated according to the position of the correction value in the isolation tree; in response to the abnormal score of the correction value corresponding to all sensors or a preset number of sensors being greater than a preset threshold, an alarm signal is generated.

[0050] The calculation method of abnormal score is: set any correction value as sample , calculate the sample Anomaly score , , where Representation sample The average path length among all isolated trees; represents the total number of samples (electrical data) in the isolated tree; is a constant representing the expected value of the path length.

[0051] The calculation formula is as follows: ; ; In the formula yes The harmonic number is the number from arrive The sum of the reciprocals of . Calculating the abnormality score of the correction value according to its position in the isolated tree is a prior art of the isolation forest algorithm, which will not be described in detail here.

[0052] If the abnormality scores of all sensors at the current moment are greater than a preset threshold (for example, the preset threshold may be 0.8), it is determined that there is a leak and an alarm signal is generated. The alarm signal may be a sound alarm (siren), a light alarm (flashing lights), or a text alarm (automatically sending an alarm SMS to a preset terminal number), etc.

[0053] The present application arranges multiple sensors, calculates the center point according to the concentration changes of the sensors, and predicts the concentration changes of harmful gases according to the distance between the multiple sensors, calculates the diffusion results of the harmful gases, and issues early warnings based on the diffusion results.

[0054] The embodiment of the present application also discloses a harmful gas detection and alarm system, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the harmful gas detection and alarm method according to the present application is implemented.

[0055] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0056] In the present application, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high bandwidth memory HBM (High Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device.

[0057] Although this specification has shown and described a plurality of embodiments of the present application, it is obvious to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will think of many changes, modifications and alternatives without departing from the thought and spirit of the present application. It should be understood that in the process of practicing the present application, various alternatives to the embodiments of the present application described herein may be adopted.

[0058] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for detecting and alarming harmful gases, characterized in that: Includes steps: Calculate the correction coefficient of the sensor acquisition value, take the product of the correction coefficient and the acquisition value as the correction value, and make an abnormal alarm based on the correction value; the calculation method of the correction coefficient is: According to the changes in the collected values ​​of the harmful gas concentration, the influence of the air flow on the sensor is calculated to obtain the influence factor; Construct a concentration sequence of the sensor acquisition values, and calculate the confidence level of the sensor based on the concentration sequence; The ratio of the sensor's confidence level to the influencing factor is normalized and used as the correction coefficient.

2. The method for detecting and alarming harmful gases according to claim 1, characterized in that: The calculation formula of impact factor is: , where Indicates The influencing factors of each sensor; Indicates The total number of acquisition moments of sensors; Indicates the collection time sequence number; Indicates The sensor at the time The collected value of harmful gas concentration; Indicates The average concentration of harmful gases collected by the sensors at all times; Represents the standard normalization function.

3. The method for detecting and alarming harmful gases according to claim 1, characterized in that: The calculation formula of impact factor is: , where Indicates The influencing factors of each sensor; Indicates The total number of acquisition moments of sensors; Indicates the collection time sequence number; Indicates The sensor at the time The collected value of harmful gas concentration; Indicates The average concentration of harmful gases collected by the sensors at all times; Represents the standard normalization function.

4. The method for detecting and alarming harmful gases according to claim 1, characterized in that: The calculation formula for the confidence level is: Constructing the concentration sequence of the sensor collection values ​​includes: calculating the Euclidean distance between any sensor and the position of other sensors, sorting the sensors in order of Euclidean distance from large to small or from small to large, taking the sensor collection values ​​as elements in the sequence, and constructing the concentration sequence of any sensor collection values.

5. The method for detecting and alarming harmful gases according to claim 1, characterized in that: When calculating the confidence level of the sensor based on the concentration sequence, the confidence level is calculated as follows: , where Indicates The sensor at the time The confidence level; Indicates The total number of data points in the concentration series for each sensor; Indicates the ordinal number of the data point in the concentration series; Expressed as a natural constant The exponential function with base ; Indicates The collected value of each sensor is consistent with the concentration sequence. The difference of the collected values; Indicates The mean of the differences between the values ​​collected by each sensor and the concentration series; Indicates The concentration sequence of the sensor A collection value; Indicates The collected values ​​of sensors; Indicates Among the sensors The Euclidean distance corresponding to the collected values.

6. The method for detecting and alarming harmful gases according to claim 1, characterized in that: When calculating the confidence level of the sensor based on the concentration sequence, the confidence level is calculated as follows: , where Indicates The sensor at the time The confidence level; Indicates The total number of data points in the concentration series for each sensor; Indicates the ordinal number of the data point in the concentration series; Expressed as a natural constant The exponential function with base ; Indicates The collected value of each sensor is consistent with the concentration sequence. The difference of the collected values; Indicates The mean of the differences between the values ​​collected by each sensor and the concentration series; Indicates The concentration sequence of the sensor A collection value; Indicates The collected values ​​of the sensors.

7. The method for detecting and alarming harmful gases according to any one of claims 1 to 6, characterized in that: The product of the correction coefficient and the collected value is used as the correction value, and an abnormal alarm is issued according to the correction value, including the steps of: An isolation tree is constructed according to the isolation forest algorithm, and the anomaly score of the correction value is calculated according to the position of the correction value in the isolation tree; In response to the abnormality scores of the correction values ​​corresponding to all sensors or a preset number of sensors being greater than a preset threshold, an alarm signal is generated.

8. A harmful gas detection and alarm system, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the harmful gas detection and alarm method according to any one of claims 1-7 is implemented.

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