A calibration system for environmental measurements

Abstract

This invention relates to the field of environmental monitoring technology and discloses an environmental measurement calibration system, including a data acquisition module, a data analysis module, and a data control module. The data analysis module calculates the dataset acquired by the data acquisition module, and the calculation results are optimization indices for air monitoring equipment, water quality monitoring equipment, soil monitoring equipment, noise monitoring equipment, and sensors. Based on the ranges of these optimization indices, the data control module adjusts the maintenance frequency, optimizes equipment status parameters, and performs periodic calibrations for the corresponding monitoring equipment in the air quality unit, water quality unit, soil unit, and noise unit, respectively.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, specifically to a verification system for environmental measurements. Background Technology

[0002] In the current context of environmental governance, the field of environmental measurement verification systems is particularly important. Environmental Technology Validation (ETV) assessment, as a crucial component of the environmental technology management system, provides important reference for domestic environmental technology validation assessment by systematically analyzing its origins, development history, and domestic and international progress. The completed ETV assessment application cases in China's medical waste sector demonstrate that this field possesses a relatively mature technological foundation and practical experience, which plays a positive role in promoting the development of environmental measurement verification technology. However, the poor dynamic measurement capabilities of existing technologies limit the accuracy and efficiency of environmental monitoring. This necessitates not only focusing on the continuous advancement and application of technology but also overcoming existing technological shortcomings to better meet the needs of environmental protection and management. Summary of the Invention

[0003] (a) Technical problems to be solved

[0004] To address the shortcomings of existing technologies, this invention provides an environmental measurement calibration system with the advantage of strong dynamic measurement capability, thus solving the problem of poor dynamic measurement capability in existing technologies.

[0005] (II) Technical Solution

[0006] To achieve the above objectives, the present invention provides the following technical solution: an environmental measurement calibration system, comprising a data acquisition module, a data analysis module, and a data control module;

[0007] The data acquisition module includes an air quality unit, a water quality unit, a soil unit, and a noise unit. The air quality unit acquires air quality data by connecting to an air quality monitor via a network, assigns numbers to the data, and then connects to the data analysis module via the network. The water quality unit acquires water quality analysis data by connecting to a water quality analyzer via a network, assigns numbers to the data, and then connects to the data analysis module via the network. The soil unit acquires soil monitoring data by connecting to a soil monitoring device via a network, assigns numbers to the data, and then connects to the data analysis module via the network. The noise unit acquires noise measurement data by connecting to a noise measuring instrument via a network and assigns numbers to the data. The data acquisition module connects to the data analysis module via the network.

[0008] The data analysis module includes a monitoring equipment optimization unit and a sensor optimization unit. The monitoring equipment optimization unit calculates the air monitoring equipment optimization index based on air quality data, water quality analysis data, soil monitoring data, and noise measurement data, respectively. Water quality monitoring equipment optimization index Soil monitoring equipment optimization index and noise monitoring equipment optimization index The sensor optimization unit is based on the air monitoring equipment optimization index. Water quality monitoring equipment optimization index Soil monitoring equipment optimization index and noise monitoring equipment optimization index Calculate the sensor optimization index The data analysis module and the data control module are connected via a network.

[0009] Preferably, the air quality unit assigns numbers to the emissions of pollutants, process dust, and toxic and harmful gases monitored online by the air monitoring equipment based on air quality data characteristics. The numbers for these emissions are as follows: , , The water quality unit assigns numbers to the chemical oxygen demand (COD), biochemical oxygen demand (BOD), heavy metal detections, and organic pollutant detections monitored online by the water quality monitoring equipment based on the characteristics of the water quality analysis data. The numbers for these parameters are as follows: , , , .

[0010] Preferably, the monitoring equipment optimization unit calculates an air monitoring equipment optimization index based on air quality data. The calculation formula is as follows:

[0011]

[0012] In the formula, This indicates the optimization index of air monitoring equipment. , , These represent the emissions of flue gas, process dust, and toxic and harmful gases from pollution sources, respectively, as monitored online by air monitoring equipment. , , These represent the actual emissions of pollutants (flue gas, process dust, and toxic and harmful gases) detected in the laboratory, respectively. , , These represent the weights of the emissions from pollution sources (flue gas, process dust, and toxic and harmful gases) in air monitoring equipment and actual laboratory testing.

[0013] Preferably, the monitoring equipment optimization unit calculates a water quality monitoring equipment optimization index based on water quality analysis data. The calculation formula is as follows:

[0014]

[0015] In the formula, This indicates the optimization index of water quality monitoring equipment. , , , These represent the chemical oxygen demand (COD), biochemical oxygen demand (BOD), heavy metal detection levels, and organic pollutant detection levels monitored online by the water quality monitoring equipment, respectively. , , , These represent the actual amounts of chemical oxygen demand (COD), biochemical oxygen demand (BOD), heavy metals, and organic pollutants detected in the laboratory, respectively. , , , These represent the weights of chemical oxygen demand (COD), biochemical oxygen demand (BOD), heavy metal detection, and organic pollutant detection in water quality monitoring equipment and actual laboratory testing, respectively.

[0016] Preferably, the soil unit is numbered according to the characteristics of soil monitoring data, specifically the content of heavy metals, petroleum hydrocarbons, organic pollutants, and inorganic pollutants monitored online by the soil monitoring equipment. The numbering of the content of heavy metals, petroleum hydrocarbons, organic pollutants, and inorganic pollutants monitored online by the soil monitoring equipment is as follows: , , , The noise unit assigns numbers to the noise source location points monitored online by the noise monitoring equipment based on the characteristics of the noise measurement data. The noise source location points monitored online by the noise monitoring equipment are numbered as follows: .

[0017] Preferably, the monitoring equipment optimization unit calculates a soil monitoring equipment optimization index based on soil monitoring data. The calculation formula is as follows:

[0018]

[0019] In the formula, This indicates the soil monitoring equipment optimization index. , , , These represent the contents of heavy metals, petroleum hydrocarbons, organic pollutants, and inorganic pollutants as monitored online by soil monitoring equipment, respectively. , , , These represent the contents of heavy metals, petroleum hydrocarbons, organic pollutants, and inorganic pollutants as actually detected online by soil monitoring equipment in the laboratory. , , , These represent the weights of heavy metal content, petroleum hydrocarbon content, organic pollutant content, and inorganic pollutant content in soil monitoring equipment and actual laboratory testing.

[0020] Preferably, the monitoring equipment optimization unit calculates the noise monitoring equipment optimization index based on the noise measurement data. The calculation formula is as follows:

[0021]

[0022] In the formula, This indicates the optimization index of noise monitoring equipment. This indicates the location of the noise source monitored online by the noise monitoring equipment. This indicates the location of the noise source as determined by the actual surveyors.

[0023] Preferably, the sensor optimization unit uses an optimization index based on the air monitoring equipment. Water quality monitoring equipment optimization index Soil monitoring equipment optimization index and noise monitoring equipment optimization index Calculate the sensor optimization index The calculation formula is as follows:

[0024]

[0025] Indicates the sensor optimization index. This indicates the optimization index of air monitoring equipment. This indicates the optimization index of water quality monitoring equipment. This indicates the soil monitoring equipment optimization index. This indicates the optimization index of noise monitoring equipment. , , , These represent the weights of air monitoring equipment, water quality monitoring equipment, soil monitoring equipment, and noise monitoring equipment in the sensor, respectively.

[0026] Preferably, the data control module optimizes the air monitoring equipment index. Water quality monitoring equipment optimization index Soil monitoring equipment optimization index and noise monitoring equipment optimization index The maintenance frequency and equipment status parameters of the corresponding monitoring equipment in the air quality unit, water quality unit, soil unit and noise unit were adjusted respectively.

[0027] Preferably, the data control module uses a sensor optimization index. The range is used to periodically calibrate the corresponding sensors in the air quality unit, water quality unit, soil unit, and noise unit.

[0028] Compared with the prior art, the present invention provides a calibration system for environmental measurement, which has the following advantages:

[0029] 1. This invention calculates the optimization index of air monitoring equipment. The system compares online monitoring data with laboratory test data in the calculation formula. Based on the calculated deviation value, the monitoring equipment optimization unit can identify the systematic and random errors of the air monitoring equipment, thereby determining the number of calibrations. The data control module then issues calibration instructions to ensure the accuracy of the monitoring data, thus solving the problem of poor dynamic measurement capability in the existing technology.

[0030] 2. This invention calculates the optimization index of noise monitoring equipment. By comparing the online monitoring data of the noise monitoring equipment with the actual noise source location points surveyed by the surveyors, the monitoring equipment optimization unit can evaluate the real-time accuracy of the monitoring equipment, which helps to ensure that the noise source is correctly identified and located, thereby providing a reliable basis for the management and control module to adjust the noise parameters.

[0031] 3. This invention calculates the sensor optimization index. The sensor optimization unit comprehensively evaluates the real-time status of air, water quality, soil and noise monitoring equipment, which helps the data control module allocate monitoring equipment resources and prioritizes the improvement of equipment that has a significant impact on the sensor optimization index, thereby improving the resource utilization efficiency of monitoring equipment and sensors. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the structure of the present invention; Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] Please see Figure 1 A verification system for environmental measurement includes a data acquisition module, a data analysis module, and a data control module;

[0035] The data acquisition module includes an air quality unit, a water quality unit, a soil unit, and a noise unit. The air quality unit connects to an air quality monitor via a network to acquire air quality data, assigns numbers to the data, and then connects to the data analysis module via the network. The water quality unit connects to a water quality analyzer via a network to acquire water quality analysis data, assigns numbers to the data, and then connects to the data analysis module via the network. The soil unit connects to a soil monitoring device via a network to acquire soil monitoring data, assigns numbers to the data, and then connects to the data analysis module via the network. The noise unit connects to a noise measuring instrument via a network to acquire noise measurement data, assigns numbers to the data, and then connects to the data analysis module via the network.

[0036] The air quality unit assigns numbers to the emissions of pollutants, including flue gas, process dust, and toxic and harmful gases, monitored online by air monitoring equipment, based on the characteristics of the air quality data. These numbers are as follows: , , The water quality unit assigns numbers to the chemical oxygen demand (COD), biochemical oxygen demand (BOD), heavy metal detections, and organic pollutant detections monitored online by the water quality monitoring equipment, based on the characteristics of the water quality analysis data. The numbers for these parameters are as follows: , , , .

[0037] The monitoring equipment optimization unit calculates the air monitoring equipment optimization index based on air quality data. The calculation formula is as follows:

[0038]

[0039] In the formula, This indicates the optimization index of air monitoring equipment. , , These represent the emissions of flue gas, process dust, and toxic and harmful gases from pollution sources, respectively, as monitored online by air monitoring equipment. , , These represent the actual emissions of pollutants (flue gas, process dust, and toxic and harmful gases) detected in the laboratory, respectively. , , These represent the weights of the emissions from pollution sources (flue gas, process dust, and toxic and harmful gases) in air monitoring equipment and actual laboratory testing.

[0040] The advantage is that it optimizes the air monitoring equipment by calculating the optimization index. The system compares online monitoring data with laboratory test data in the calculation formula. Based on the calculated deviation value, the monitoring equipment optimization unit can identify the systematic and random errors of the air monitoring equipment, thereby determining the number of calibrations. The data control module then issues calibration instructions to ensure the accuracy of the monitoring data, thus solving the problem of poor dynamic measurement capability in the existing technology.

[0041] The monitoring equipment optimization unit calculates the water quality monitoring equipment optimization index based on water quality analysis data. The calculation formula is as follows:

[0042]

[0043] In the formula, This indicates the optimization index of water quality monitoring equipment. , , , These represent the chemical oxygen demand (COD), biochemical oxygen demand (BOD), heavy metal detection levels, and organic pollutant detection levels monitored online by the water quality monitoring equipment, respectively. , , , These represent the actual amounts of chemical oxygen demand (COD), biochemical oxygen demand (BOD), heavy metals, and organic pollutants detected in the laboratory, respectively. , , , These represent the weights of chemical oxygen demand (COD), biochemical oxygen demand (BOD), heavy metal detection, and organic pollutant detection in water quality monitoring equipment and actual laboratory testing, respectively.

[0044] The advantage is that it optimizes the water quality monitoring equipment by calculating the optimization index. By comparing online monitoring data with laboratory test data, and incorporating the importance of chemical oxygen demand (COD), biochemical oxygen demand (BOD), heavy metal detection, and organic pollutant detection from the online water quality monitoring equipment into the calculation, the water quality status can be assessed, which helps the control module issue instructions to correct the systematic errors of the water quality monitoring equipment.

[0045] Soil units are numbered based on the characteristics of soil monitoring data, specifically the content of heavy metals, petroleum hydrocarbons, organic pollutants, and inorganic pollutants monitored online by soil monitoring equipment. The numbers for the heavy metals, petroleum hydrocarbons, organic pollutants, and inorganic pollutants monitored online by soil monitoring equipment are as follows: , , , The noise unit assigns numbers to the noise source location points monitored online by the noise monitoring equipment based on the characteristics of the noise measurement data. The noise source location point numbers monitored online by the noise monitoring equipment are as follows: .

[0046] The monitoring equipment optimization unit calculates the soil monitoring equipment optimization index based on soil monitoring data. The calculation formula is as follows:

[0047]

[0048] In the formula, This indicates the soil monitoring equipment optimization index. , , , These represent the contents of heavy metals, petroleum hydrocarbons, organic pollutants, and inorganic pollutants as monitored online by soil monitoring equipment, respectively. , , , These represent the contents of heavy metals, petroleum hydrocarbons, organic pollutants, and inorganic pollutants as actually detected online by soil monitoring equipment in the laboratory. , , , These represent the weights of heavy metal content, petroleum hydrocarbon content, organic pollutant content, and inorganic pollutant content in soil monitoring equipment and actual laboratory testing.

[0049] The advantage is that it optimizes the soil monitoring equipment by calculating the index. This helps the monitoring equipment optimization unit to promptly detect performance degradation or malfunctions of the monitoring equipment, thereby transmitting the information to the control module and issuing instructions for maintenance or replacement to ensure the stable operation of the monitoring system.

[0050] The monitoring equipment optimization unit calculates the noise monitoring equipment optimization index based on noise measurement data. The calculation formula is as follows:

[0051]

[0052] In the formula, This indicates the optimization index of noise monitoring equipment. This indicates the location of the noise source monitored online by the noise monitoring equipment. This indicates the location of the noise source as determined by the actual surveyors.

[0053] The advantage is that it optimizes the noise monitoring equipment by calculating the optimization index. By comparing the online monitoring data of the noise monitoring equipment with the actual noise source location points surveyed by the surveyors, the monitoring equipment optimization unit can evaluate the real-time accuracy of the monitoring equipment, which helps to ensure that the noise source is correctly identified and located, thereby providing a reliable basis for the management and control module to adjust the noise parameters.

[0054] The sensor optimization unit optimizes the air monitoring equipment based on the optimization index. Water quality monitoring equipment optimization index Soil monitoring equipment optimization index and noise monitoring equipment optimization index Calculate the sensor optimization index The calculation formula is as follows:

[0055]

[0056] Indicates the sensor optimization index. This indicates the optimization index of air monitoring equipment. This indicates the optimization index of water quality monitoring equipment. This indicates the soil monitoring equipment optimization index. This indicates the optimization index of noise monitoring equipment. , , , These represent the weights of air monitoring equipment, water quality monitoring equipment, soil monitoring equipment, and noise monitoring equipment in the sensor, respectively.

[0057] The advantage is that it optimizes the sensor index by calculating the sensor. The sensor optimization unit comprehensively evaluates the real-time status of air, water quality, soil and noise monitoring equipment, which helps the data control module allocate monitoring equipment resources and prioritizes the improvement of equipment that has a significant impact on the sensor optimization index, thereby improving the resource utilization efficiency of monitoring equipment and sensors.

[0058] The data control module optimizes the index based on the air monitoring equipment. Water quality monitoring equipment optimization index Soil monitoring equipment optimization index and noise monitoring equipment optimization index The maintenance frequency and equipment status parameters of the corresponding monitoring equipment in the air quality unit, water quality unit, soil unit and noise unit were adjusted respectively.

[0059] When the air monitoring equipment optimization index Water quality monitoring equipment optimization index Soil monitoring equipment optimization index and noise monitoring equipment optimization index When all values ​​are less than 0.5, the maintenance frequency is adjusted to once every 10 days, and the equipment status is adjusted to low power consumption mode to extend the life of the monitoring equipment.

[0060] The data control module optimizes based on the sensor index. The range is used to periodically calibrate the corresponding sensors in the air quality unit, water quality unit, soil unit, and noise unit.

[0061] When the sensor optimization index When the value is between 0.8 and 0.9, the calibration period of 6 months will be changed to a regular calibration of 3 months to ensure the effectiveness of data monitoring.

[0062] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A calibration system for environmental measurement, characterized in that, It includes a data acquisition module, a data analysis module, and a data control module; The data acquisition module includes an air quality unit, a water quality unit, a soil unit, and a noise unit. The air quality unit acquires air quality data by connecting to an air quality monitor via a network, assigns numbers to the data, and then connects to the data analysis module via the network. The water quality unit acquires water quality analysis data by connecting to a water quality analyzer via a network, assigns numbers to the data, and then connects to the data analysis module via the network. The soil unit acquires soil monitoring data by connecting to a soil monitoring device via a network, assigns numbers to the data, and then connects to the data analysis module via the network. The noise unit acquires noise measurement data by connecting to a noise measuring instrument via a network and assigns numbers to the data. The data acquisition module connects to the data analysis module via the network. The data analysis module includes a monitoring equipment optimization unit and a sensor optimization unit. The monitoring equipment optimization unit calculates the air monitoring equipment optimization index based on air quality data, water quality analysis data, soil monitoring data, and noise measurement data, respectively. Water quality monitoring equipment optimization index Soil monitoring equipment optimization index and noise monitoring equipment optimization index The sensor optimization unit is based on the air monitoring equipment optimization index. Water quality monitoring equipment optimization index Soil monitoring equipment optimization index and noise monitoring equipment optimization index Calculate the sensor optimization index The data analysis module and the data control module are connected via a network; The air quality unit assigns numbers to the emissions of pollutants, process dust, and toxic and harmful gases monitored online by the air monitoring equipment, based on air quality data characteristics. These numbers are as follows: , , The water quality unit assigns numbers to the chemical oxygen demand (COD), biochemical oxygen demand (BOD), heavy metal detections, and organic pollutant detections monitored online by the water quality monitoring equipment based on the characteristics of the water quality analysis data. The numbers for these parameters are as follows: , , , ; The soil unit is numbered according to the characteristics of soil monitoring data, specifically the content of heavy metals, petroleum hydrocarbons, organic pollutants, and inorganic pollutants monitored online by the soil monitoring equipment. The numbers for the heavy metals, petroleum hydrocarbons, organic pollutants, and inorganic pollutants monitored online by the soil monitoring equipment are as follows: , , , The noise unit assigns numbers to the noise source location points monitored online by the noise monitoring equipment based on the characteristics of the noise measurement data. The noise source location points monitored online by the noise monitoring equipment are numbered as follows: ; The monitoring equipment optimization unit calculates the soil monitoring equipment optimization index based on soil monitoring data. The calculation formula is as follows: In the formula, This indicates the soil monitoring equipment optimization index. , , , These represent the contents of heavy metals, petroleum hydrocarbons, organic pollutants, and inorganic pollutants as monitored online by soil monitoring equipment, respectively. , , , These represent the contents of heavy metals, petroleum hydrocarbons, organic pollutants, and inorganic pollutants as actually detected online by soil monitoring equipment in the laboratory. , , , These represent the weights of heavy metal content, petroleum hydrocarbon content, organic pollutant content, and inorganic pollutant content in soil monitoring equipment and actual laboratory testing, respectively. The monitoring equipment optimization unit calculates the noise monitoring equipment optimization index based on the noise measurement data. The calculation formula is as follows: In the formula, This indicates the optimization index of noise monitoring equipment. This indicates the location of the noise source monitored online by the noise monitoring equipment. This indicates the location of the noise source as actually surveyed by the surveyors; The sensor optimization unit optimizes the air monitoring equipment based on the optimization index. Water quality monitoring equipment optimization index Soil monitoring equipment optimization index and noise monitoring equipment optimization index Calculate the sensor optimization index The calculation formula is as follows: Indicates the sensor optimization index. This indicates the optimization index of air monitoring equipment. This indicates the optimization index of water quality monitoring equipment. This indicates the soil monitoring equipment optimization index. This indicates the optimization index of noise monitoring equipment. , , , These represent the weights of air monitoring equipment, water quality monitoring equipment, soil monitoring equipment, and noise monitoring equipment in the sensor, respectively.

2. The environmental measurement verification system according to claim 1, characterized in that: The monitoring equipment optimization unit calculates an air monitoring equipment optimization index based on air quality data. The calculation formula is as follows: In the formula, This indicates the optimization index of air monitoring equipment. , , These represent the emissions of flue gas, process dust, and toxic and harmful gases from pollution sources, respectively, as monitored online by air monitoring equipment. , , These represent the actual emissions of pollutants (flue gas, process dust, and toxic and harmful gases) detected in the laboratory, respectively. , , These represent the weights of the emissions from pollution sources (flue gas, process dust, and toxic and harmful gases) in air monitoring equipment and actual laboratory testing.

3. The environmental measurement verification system according to claim 1, characterized in that: The monitoring equipment optimization unit calculates the water quality monitoring equipment optimization index based on water quality analysis data. The calculation formula is as follows: In the formula, This indicates the optimization index of water quality monitoring equipment. , , , These represent the chemical oxygen demand (COD), biochemical oxygen demand (BOD), heavy metal detection levels, and organic pollutant detection levels monitored online by the water quality monitoring equipment, respectively. , , , These represent the actual amounts of chemical oxygen demand (COD), biochemical oxygen demand (BOD), heavy metals, and organic pollutants detected in the laboratory, respectively. , , , These represent the weights of chemical oxygen demand (COD), biochemical oxygen demand (BOD), heavy metal detection, and organic pollutant detection in water quality monitoring equipment and actual laboratory testing, respectively.

4. The environmental measurement verification system according to claim 1, characterized in that: The data control module optimizes the air monitoring equipment index. Water quality monitoring equipment optimization index Soil monitoring equipment optimization index and noise monitoring equipment optimization index The maintenance frequency and equipment status parameters of the corresponding monitoring equipment in the air quality unit, water quality unit, soil unit and noise unit were adjusted respectively.

5. The environmental measurement verification system according to claim 1, characterized in that: The data control module is based on the sensor optimization index. The range is used to periodically calibrate the corresponding sensors in the air quality unit, water quality unit, soil unit, and noise unit.

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