A method and device for monitoring carbon emissions

Abstract

The present invention discloses a carbon emission monitoring method and device, and relates to the technical field of environmental monitoring. The carbon emission monitoring method comprises the following steps: S1, data integrity assessment; S2, data accuracy assessment; S3, optimization assessment. The present invention performs data integrity assessment on the first carbon emission monitoring data obtained to obtain an integrity assessment value, determines whether to perform data integrity optimization based on the integrity assessment value, and then performs data accuracy assessment on the second carbon emission monitoring data obtained to obtain an accuracy assessment value, determines whether to perform data accuracy optimization based on the accuracy assessment value, and finally performs an assessment based on the sensor accuracy, the integrity assessment value obtained after optimization, and the accuracy assessment value obtained after optimization to obtain an optimization assessment value, determines whether to perform feedback based on the optimization assessment value, thereby achieving the effect of improving the data accuracy of carbon emission monitoring, and solving the problem of low data accuracy of carbon emission monitoring in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to a carbon emission monitoring method and device. Background Art

[0002] Excessive emissions of carbon dioxide, methane and other greenhouse gases are considered to be one of the main causes of global climate change. These gases can absorb and reradiate heat from the earth's surface, causing climate warming, which in turn affects various fields such as ecosystems, agriculture, energy and water resources. Therefore, the monitoring and accounting of greenhouse gas emissions is particularly important. It is not only the basis and prerequisite for achieving carbon emission reduction, but also an important basis for the smooth progress of carbon market transactions. With the growing global demand for climate change response, carbon emission monitoring technology has also ushered in rapid development. Through carbon emission monitoring methods and devices, carbon emissions can be accurately calculated, thereby achieving effective monitoring and management of carbon emissions.

[0003] Existing technology collects and detects gas composition and concentration, and then analyzes the gas composition and concentration to obtain carbon emissions, thereby achieving effective monitoring and management of carbon emissions.

[0004] For example, the carbon emission monitoring device and carbon emission monitoring method disclosed in the invention patent with publication number: CN116930421A include: a data acquisition module, used to collect parameters in the air in the area, including humidity sd, temperature wd, wind speed fs and atmospheric pressure yq for N consecutive days; a data transmission module, used to transmit the humidity sd, temperature wd, wind speed fs and atmospheric pressure yq collected by the data acquisition module to a data analysis and processing module; a data analysis and processing module, used to analyze and process the parameters in the air to obtain the CO2 concentration coefficient XSnd and the CO2 concentration change rate BHLnd, and perform correlation analysis on the CO2 concentration coefficient XSnd and the CO2 concentration change rate BHLnd to obtain the carbon emission coefficient XS; an execution module.

[0005] For example, the invention patent with announcement number: CN116859001B announces an online carbon emission monitoring platform and analysis method, which includes: a monitoring module, a preprocessing module, a temperature calibration module, a data transmission module, a data processing module and a data analysis module. By measuring the carbon dioxide concentration at different points on a straight line, the distance and data detected by each concentration detection unit are used to form a concentration array, and the concentration diffusion anomaly of carbon dioxide is analyzed based on the concentration array to generate a wind speed diffusion coefficient. The temperature of the monitoring environment is measured, combined with the average temperature, the temperature diffusion coefficient is calculated, and a Gaussian function model is used to fit the diffusion of carbon dioxide at the monitoring point, and a carbon emission evaluation coefficient is constructed. The carbon emission evaluation coefficient can fit the total concentration of carbon dioxide within a distance, and the temperature diffusion coefficient and the wind speed diffusion coefficient are used to correct the carbon emission evaluation coefficient.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the prior art, due to inaccurate data acquisition and abnormal data transmission during the data collection process, the integrity and accuracy of the carbon emission data obtained by the monitoring equipment are affected, and there is a problem of low data accuracy in carbon emission monitoring. Summary of the invention

[0008] The embodiments of the present application provide a carbon emission monitoring method and device, thereby solving the problem of low data accuracy in carbon emission monitoring in the prior art and improving the data accuracy in carbon emission monitoring.

[0009] An embodiment of the present application provides a carbon emission monitoring method, comprising the following steps: S1, performing data integrity assessment based on acquired first carbon emission monitoring data to obtain an integrity assessment value, judging whether to perform data integrity optimization based on the integrity assessment value, the first carbon emission monitoring data being used to assess the integrity of the carbon emission monitoring data, and the integrity assessment value being used to assess the integrity of the carbon emission monitoring data; S2, performing data accuracy assessment based on acquired second carbon emission monitoring data to obtain an accuracy assessment value, judging whether to perform data accuracy optimization based on the accuracy assessment value, the second carbon emission monitoring data being used to assess the accuracy of the carbon emission monitoring data, and the accuracy assessment value being used to assess the accuracy of the carbon emission monitoring data; S3, performing an assessment based on sensor accuracy, the integrity assessment value obtained after optimization, and the accuracy assessment value obtained after optimization to obtain an optimization assessment value, judging whether to perform feedback based on the optimization assessment value, and the optimization assessment value being used to assess the effectiveness of the optimization measures.

[0010] Furthermore, the first carbon emission monitoring data includes sensor failure rate, data loss rate, sensor accuracy, sensor response time, deployment density, coverage, and key emission source identification rate; the second carbon emission monitoring data includes sensor drift rate, noise, monitoring point temperature, and monitoring point wind speed.

[0011] Furthermore, the specific method for performing data integrity assessment on the basis of the acquired first carbon emission monitoring data to obtain an integrity assessment value is as follows: obtain the sensor failure rate deviation based on the sensor failure rate and the preset sensor failure rate in the database; obtain the data loss rate deviation based on the data loss rate and the preset data loss rate in the database; obtain the sensor response time deviation based on the sensor response time and the preset sensor response time in the database; obtain the sensor accuracy deviation based on the sensor accuracy and the preset sensor accuracy in the database; process the sensor failure rate deviation, the data loss rate deviation, the sensor response time deviation and the sensor accuracy deviation to obtain the sensor performance coefficient; obtain the sensor deployment density deviation based on the sensor deployment density and the preset sensor deployment density in the database; obtain the sensor coverage deviation based on the sensor coverage and the preset sensor coverage in the database; obtain the key emission source identification rate deviation based on the key emission source identification rate and the preset key emission source identification rate in the database; process the sensor failure rate deviation, the sensor deployment density deviation, the sensor coverage deviation and the key emission source identification rate deviation to obtain the sensor deployment coefficient; process the network stability factor, the sensor performance coefficient and the sensor deployment coefficient in the database to obtain the integrity assessment value.

[0012] Furthermore, the specific process of judging whether to perform data integrity optimization based on the integrity evaluation value is as follows: judging whether the integrity evaluation value is less than a preset integrity threshold in the database, if the integrity evaluation value is less than the preset integrity threshold in the database, performing data integrity optimization, otherwise not performing data integrity optimization; the data integrity optimization includes sensor performance optimization, data transmission optimization and sensor deployment optimization; the data transmission optimization includes data compensation and adding redundant sensors; the sensor deployment optimization includes optimizing sensor layout and increasing coverage density.

[0013] Furthermore, the sensor deployment optimization includes optimizing sensor layout and increasing coverage density; the optimized sensor layout identifies data blind spots through map analysis tools and adds sensors in the data blind spots; the increased coverage density means adding gas analyzers in areas where the number of sensor coverage is less than the preset number of sensors and increasing the number of sensors in key areas according to seasonal changes.

[0014] Further, the specific method for performing data accuracy assessment based on the acquired second carbon emission monitoring data to obtain an accuracy assessment value is as follows: obtaining a sensor drift rate deviation based on a sensor drift rate and a preset sensor drift rate in a database; when the monitoring point temperature is greater than the preset monitoring point temperature maximum value in the database, obtaining a monitoring point temperature deviation based on the monitoring point temperature and the preset monitoring point temperature maximum value in the database, otherwise the monitoring point temperature deviation is recorded as 0; processing the sensor drift rate deviation, the noise deviation, the monitoring point wind speed deviation and the monitoring point temperature deviation to obtain an accuracy assessment value, wherein the noise deviation is obtained by processing a ratio operation of the noise and the preset noise, and the monitoring point wind speed deviation is obtained by processing a ratio operation of the monitoring point wind speed and the preset monitoring point wind speed; the specific method for obtaining the accuracy assessment value is:

[0015]

[0016] In the formula, ZQ represents the accuracy evaluation value, represents the optimized integrity assessment value, PYI represents the sensor drift rate, ZSH represents the noise, WDP represents the temperature deviation of the monitoring point, FSU represents the wind speed of the monitoring point, Indicates the preset sensor drift rate, represents the preset noise, Indicates the wind speed at the preset monitoring point.

[0017] Furthermore, the specific process of judging whether to perform data accuracy optimization based on the accuracy evaluation value is as follows: judging whether the accuracy evaluation value is less than a preset accuracy threshold in the database, if the accuracy evaluation value is less than the preset accuracy threshold, performing sensor optimization, otherwise not performing sensor optimization; when the accuracy evaluation value obtained after sensor optimization is less than the preset accuracy threshold in the database, performing data smoothing, otherwise terminating the data accuracy optimization, the sensor optimization includes calibrating the sensor and replacing the sensor.

[0018] Furthermore, the specific method for evaluating and obtaining the optimized evaluation value according to the sensor accuracy, the integrity evaluation value obtained after optimization and the accuracy evaluation value obtained after optimization is as follows: obtain the sensor accuracy deviation according to the sensor accuracy and the preset sensor accuracy in the database; obtain the accuracy optimization coefficient according to the optimized accuracy evaluation value, the accuracy evaluation value and the preset accuracy threshold in the database; obtain the integrity optimization coefficient according to the optimized integrity evaluation value, the integrity evaluation value and the preset integrity threshold in the database; process the sensor accuracy deviation, the accuracy optimization coefficient and the integrity optimization coefficient to obtain the optimized evaluation value.

[0019] Furthermore, the specific process of judging whether to provide feedback based on the optimization evaluation value is as follows: judging whether the optimization evaluation value is less than a preset optimization threshold in the database: if the optimization evaluation value is less than the preset optimization threshold in the database, performing environmental compensation; if the optimization evaluation value is not less than the preset optimization threshold in the database, no feedback is provided; if the optimization evaluation value obtained after environmental compensation is less than the preset optimization threshold in the database, providing feedback, otherwise ending the feedback.

[0020] An embodiment of the present application provides a carbon emission monitoring device, including: a data integrity assessment module, a data accuracy assessment module and an optimization assessment module: wherein the data integrity assessment module is used to perform data integrity assessment according to the acquired first carbon emission monitoring data to obtain an integrity assessment value, and judge whether to perform data integrity optimization based on the integrity assessment value, the first carbon emission monitoring data is used to assess the integrity of the carbon emission monitoring data, and the integrity assessment value is used to assess the integrity of the carbon emission monitoring data; the data accuracy assessment module is used to perform data accuracy assessment according to the acquired second carbon emission monitoring data to obtain an accuracy assessment value, and judge whether to perform data accuracy optimization based on the accuracy assessment value, the second carbon emission monitoring data is used to assess the accuracy of the carbon emission monitoring data, and the accuracy assessment value is used to assess the accuracy of the carbon emission monitoring data; the optimization assessment module is used to perform assessment according to the sensor accuracy, the integrity assessment value obtained after optimization and the accuracy assessment value obtained after optimization to obtain an optimization assessment value, judge whether to perform feedback based on the optimization assessment value, and the optimization assessment value is used to assess the effectiveness of the optimization measures.

[0021] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0022] 1. Determine whether to perform data integrity optimization based on the integrity assessment value obtained from the first carbon emission monitoring data, then determine whether to perform data accuracy optimization based on the accuracy assessment value obtained from the second carbon emission monitoring data, and finally determine whether to provide feedback based on the optimized assessment value obtained based on the sensor accuracy, the optimized integrity assessment value, and the optimized accuracy assessment value, thereby improving the integrity of the carbon emission monitoring data, and further improving the data accuracy of carbon emission monitoring, effectively solving the problem of low data accuracy of carbon emission monitoring in the prior art.

[0023] 2. The sensor performance coefficient is obtained by processing the sensor failure rate deviation, data loss rate deviation, sensor response time deviation and sensor accuracy deviation. Then, the sensor failure rate deviation, sensor deployment density deviation, sensor coverage deviation and key emission source identification rate deviation are processed to obtain the sensor deployment coefficient. Finally, the network stability factor, sensor performance coefficient and sensor deployment coefficient in the database are processed to obtain the integrity assessment value, thereby quantitatively evaluating the integrity of the data and improving the data integrity.

[0024] 3. The sensor drift rate deviation is obtained through the sensor drift rate and the preset sensor drift rate in the database. Then, when the monitoring point temperature is greater than the preset monitoring point temperature maximum value, the monitoring point temperature deviation is obtained according to the monitoring point temperature and the preset monitoring point temperature maximum value, otherwise the monitoring point temperature deviation is recorded as 0. Finally, the sensor drift rate deviation, noise deviation, monitoring point wind speed deviation and monitoring point temperature deviation are processed to obtain the accuracy evaluation value, thereby quantitatively evaluating the accuracy of the data and achieving improved data accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A flow chart of a carbon emission monitoring method provided in an embodiment of the present application;

[0026] Figure 2 A schematic diagram showing changes in the integrity assessment value provided in an embodiment of the present application along with the sensor failure rate;

[0027] Figure 3 A schematic diagram showing changes in the integrity assessment value along with the data loss rate provided in an embodiment of the present application;

[0028] Figure 4 A schematic diagram showing changes in the integrity assessment value provided in an embodiment of the present application along with the sensor response time;

[0029] Figure 5 A schematic diagram showing the change of the integrity assessment value provided in the embodiment of the present application along with the sensor accuracy;

[0030] Figure 6 A schematic diagram of the structure of a carbon emission monitoring device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] The embodiments of the present application solve the problem of low data accuracy in the prior art for carbon emission monitoring by providing a carbon emission monitoring method and device. The method comprises: performing a data integrity assessment on the acquired first carbon emission monitoring data to obtain an integrity assessment value, determining whether to perform data integrity optimization based on the integrity assessment value, and then performing a data accuracy assessment on the acquired second carbon emission monitoring data to obtain an accuracy assessment value. Based on the accuracy assessment value, determining whether to perform data accuracy optimization. Finally, an evaluation is performed based on the sensor accuracy, the integrity assessment value obtained after optimization, and the accuracy assessment value obtained after optimization to obtain an optimization assessment value. Based on the optimization assessment value, determining whether to perform feedback, thereby improving the data accuracy of carbon emission monitoring.

[0032] The technical solution in the embodiment of the present application is to solve the problem of low data accuracy of the above-mentioned carbon emission monitoring, and the overall idea is as follows:

[0033] The integrity assessment value obtained is used to determine whether to optimize the data integrity, and then the accuracy assessment value obtained is used to determine whether to optimize the data accuracy. Finally, based on the optimization assessment value obtained, it is determined whether to provide feedback, thereby achieving the effect of improving the data accuracy of carbon emission monitoring.

[0034] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0035] like Figure 1 As shown, it is a flow chart of a carbon emission monitoring method provided in an embodiment of the present application, and the method includes the following steps: S1, data integrity assessment: performing data integrity assessment based on the acquired first carbon emission monitoring data to obtain an integrity assessment value, and judging whether to perform data integrity optimization based on the integrity assessment value, the first carbon emission monitoring data is used to evaluate the integrity of the carbon emission monitoring data, and the integrity assessment value is used to evaluate the integrity of the carbon emission monitoring data; S2, data accuracy assessment: performing data accuracy assessment based on the acquired second carbon emission monitoring data to obtain an accuracy assessment value, and judging whether to perform data accuracy optimization based on the accuracy assessment value, the second carbon emission monitoring data is used to evaluate the accuracy of the carbon emission monitoring data, and the accuracy assessment value is used to evaluate the accuracy of the carbon emission monitoring data; S3, optimization evaluation: performing an evaluation based on the sensor accuracy, the integrity assessment value obtained after optimization, and the accuracy assessment value obtained after optimization to obtain an optimization assessment value, judging whether to perform feedback based on the optimization assessment value, and the optimization assessment value is used to evaluate the effectiveness of the optimization measures.

[0036] It should be added that the first carbon emission monitoring data includes sensor failure rate, data loss rate, sensor accuracy, sensor response time, deployment density, coverage, and key emission source identification rate; the second carbon emission monitoring data includes sensor drift rate, noise, monitoring point temperature and monitoring point wind speed.

[0037] Specifically, the sensor failure rate is obtained by statistically analyzing the failure events of the gas analyzer in the sensor monitoring system to obtain the number of failures and the total monitoring time, and performing a ratio operation on the obtained number of failures and the total monitoring time.

[0038] The data loss rate is obtained by statistically analyzing the timestamps of the data collected by the gas analyzer in the sensor monitoring system to obtain the data loss duration and the total collection time, and performing a ratio operation on the obtained data loss duration and the total collection time.

[0039] The sensor accuracy is obtained by calculating the ratio of the gas concentration collected by the gas analyzer in the sensor monitoring system to the standard gas concentration.

[0040] The sensor response time is obtained by recording the time difference from the gas analyzer receiving the signal to outputting the measurement data in the sensor monitoring system.

[0041] The sensor deployment density is obtained by calculating the ratio of the number of gas analyzers in the sensor monitoring system to the area of ​​the monitoring area.

[0042] The Geographic Information System (GIS) combines the installation location and signal range of the gas analyzer to obtain the sensor coverage.

[0043] The sensor drift rate is obtained by processing the gas concentration measured by the gas analyzer and the standard gas concentration within the preset time period; the noise is obtained by calculating the standard deviation of the gas concentration data measured by the gas analyzer; the temperature of the monitoring point is obtained by measuring the temperature of the monitoring point with a temperature sensor; and the wind speed of the monitoring point is obtained by measuring the wind speed of the monitoring point with an anemometer.

[0044] The key emission source identification rate is obtained by using a geographic information system to conduct spatial analysis based on the known locations of pollution sources and real-time gas concentrations, identify possible key emission sources, and record the number of identified key emission sources. The ratio of the number of identified key emission sources to the total number of emission sources is then calculated.

[0045] In this embodiment, the standard gas concentration represents the gas concentration obtained by a standard gas analyzer (a calibrated gas analyzer); through the above steps, the integrity and accuracy of the carbon emission monitoring data are systematically evaluated and optimized; data integrity optimization ensures the integrity of the data, and data accuracy optimization ensures the accuracy and reliability of the data, thereby improving the integrity of the carbon emission monitoring data, which in turn is conducive to more accurate monitoring of the carbon emission monitoring data.

[0046] Further, the specific method of performing data integrity assessment on the first carbon emission monitoring data to obtain the integrity assessment value is as follows: obtaining the sensor failure rate deviation (i.e., the sensor failure rate deviation in the specific method of obtaining the integrity assessment value) according to the sensor failure rate and the preset sensor failure rate in the database ), the sensor failure rate deviation is obtained by processing the ratio of the sensor failure rate to the preset sensor failure rate; the data loss rate deviation is obtained according to the data loss rate and the preset data loss rate in the database (i.e., the specific method of obtaining the integrity assessment value ), the data loss rate deviation is obtained by processing the ratio of the data loss rate to the preset data loss rate; the sensor response time deviation is obtained according to the sensor response time and the preset sensor response time in the database (i.e., the specific method of obtaining the integrity assessment value ), the sensor response time deviation is obtained by calculating the ratio of the sensor response time to the preset sensor response time; the sensor accuracy deviation (i.e., the specific method of obtaining the integrity assessment value) is obtained according to the sensor accuracy and the preset sensor accuracy in the database. ), the sensor accuracy deviation is obtained by processing the ratio of the sensor accuracy and the preset sensor accuracy; the sensor failure rate deviation, data loss rate deviation, sensor response time deviation and sensor accuracy deviation are processed to obtain the sensor performance coefficient (i.e., the specific method of obtaining the integrity assessment value). ); obtain the sensor deployment density deviation according to the sensor deployment density and the preset sensor deployment density in the database (i.e., the specific method of obtaining the integrity assessment value ), the sensor deployment density deviation is obtained by processing the ratio of the sensor deployment density and the preset sensor deployment density; the sensor coverage deviation is obtained according to the sensor coverage and the preset sensor coverage in the database (i.e., the specific method of obtaining the integrity assessment value ), the sensor coverage deviation is obtained by calculating the ratio of the sensor coverage to the preset sensor coverage; the key emission source identification rate deviation (i.e., the specific method of obtaining the integrity assessment value) is obtained according to the key emission source identification rate and the preset key emission source identification rate in the database. ), the key emission source identification rate deviation is obtained by processing the ratio of the key emission source identification rate and the preset key emission source identification rate; the sensor failure rate deviation, sensor deployment density deviation, sensor coverage deviation and key emission source identification rate deviation are processed to obtain the sensor deployment coefficient (i.e., the specific method for obtaining the integrity assessment value). ); The network stability factor, sensor performance coefficient and sensor deployment coefficient in the database are processed to obtain the integrity assessment value.

[0047] The specific method for obtaining the integrity assessment value is as follows:

[0048] In the formula, i represents the number of the sensor, , N represents the total number of sensors, WZ represents the integrity assessment value, represents the sensor performance coefficient, represents the sensor deployment factor, represents the sensor failure rate of the ith sensor, represents the data loss rate of the i-th sensor, represents the sensor response time of the i-th sensor, represents the sensor accuracy of the i-th sensor, Indicates the preset sensor failure rate, Indicates the preset data loss rate, Indicates the preset sensor response time, represents the preset sensor accuracy, BSM represents the sensor deployment density, FGW represents the sensor coverage, PFS represents the key emission source identification rate, Indicates the preset sensor deployment density, Indicates the preset sensor coverage, represents the preset key emission source identification rate, and α represents the network stability factor.

[0049] In this embodiment, the preset sensor failure rate is represented by the average value of the sensor failure rate in the historical time period; the preset data loss rate is represented by the average value of the data loss rate in the historical time period; the preset sensor accuracy is represented by the average value of the sensor accuracy in the historical time period; the preset sensor response time is represented by the average value of the sensor response time in the historical time period; the preset deployment density is represented by the average value of the deployment density in the historical time period; the preset coverage range is represented by the average value of the coverage range in the historical time period; and the preset key emission source identification rate is represented by the average value of the key emission source identification rate in the historical time period.

[0050] The network stability factor is obtained from the database and represents the degree of influence of network data (network failure rate, transmission delay, packet loss rate) on the integrity assessment value. For example, a mapping set of network data and its corresponding influencing factors is constructed based on the relationship between historical network data and data loss rate, and real-time network data is input into the mapping set to obtain the corresponding network stability factor; the network failure rate is obtained by performing a ratio operation on the number of failures obtained by statistically analyzing network failure events in network monitoring tools (such as Zabbix) and the total detection time; the transmission delay is obtained by performing an interpolation operation on the sending time and receiving time of the obtained data packets; the packet loss rate is obtained by performing a ratio operation on the number of lost data packets recorded by network monitoring tools (such as Wireshark) and the total number of sent data packets.

[0051] The integrity assessment value in this algorithm involves processing multiple independent variables (sensor failure rate, data loss rate, sensor accuracy, sensor response time, deployment density, coverage range and key emission source identification rate), and there is a mutual influence relationship between these independent variables; sensor failure may cause data to be unable to be collected or transmitted normally, thereby increasing the risk of data loss, that is, an increase in sensor failure rate may lead to an increase in data loss rate; sensor failure may lead to a decrease in accuracy, and the greater the sensor failure rate, the lower the sensor accuracy may be; the failure may cause the sensor response time to become longer, because the fault state may affect the response speed of the sensor element or the execution efficiency of the signal processing algorithm, that is, an increase in the sensor failure rate may lead to an increase in the sensor response time.

[0052] Increasing the deployment density of sensors may lead to an expansion of coverage; increasing the deployment density of sensors may improve the identification rate of key emission sources; the larger the coverage, the more emission sources the sensor network can monitor, which may improve the identification rate of key emission sources; in summary, the integrity assessment value is negatively correlated with sensor failure rate, data loss rate, and sensor response time, and is positively correlated with sensor accuracy, deployment density, coverage, and identification rate of key emission sources.

[0053] For example, the preset sensor failure rate is 0.05%, the preset data loss rate is 0.1%, the preset sensor response time is 0.2s, the preset sensor accuracy is 0.3%, and the network stability factor is 1. Figure 2 The figure shows a schematic diagram of the change of the integrity evaluation value provided in the embodiment of the present application with the sensor failure rate. When the data loss rate is 0.1%, the sensor response time is 0.2s, and the sensor accuracy is 0.3%, the integrity evaluation value decreases with the increase of the sensor failure rate.

[0054] like Figure 3The figure shows a schematic diagram of the change of the integrity assessment value provided in the embodiment of the present application with the data loss rate. When the sensor failure rate is 0.05%, the sensor response time is 0.2s, and the sensor accuracy is 0.3%, the integrity assessment value decreases with the increase of the data loss rate.

[0055] like Figure 4 The figure shows a schematic diagram of the change of the integrity assessment value provided in the embodiment of the present application with the sensor response time. When the sensor failure rate is 0.05%, the data loss rate is 0.1%, and the sensor accuracy is 0.3%, the integrity assessment value decreases as the sensor response time increases.

[0056] like Figure 5 The figure shows a schematic diagram of the change of the integrity evaluation value provided in the embodiment of the present application with the sensor accuracy. When the sensor failure rate is 0.05%, the data loss rate is 0.1%, and the sensor response time is 0.2s, the integrity evaluation value increases with the increase of the sensor accuracy.

[0057] Through the above steps, the integrity of the data is quantitatively evaluated, thereby achieving the improvement of data integrity in carbon emission monitoring.

[0058] Furthermore, the specific process of judging whether to perform data integrity optimization based on the integrity assessment value is as follows: judging whether the integrity assessment value is less than a preset integrity threshold in the database, if the integrity assessment value is less than the preset integrity threshold in the database, performing data integrity optimization, otherwise not performing data integrity optimization; data integrity optimization includes sensor performance optimization, data transmission optimization and sensor deployment optimization; data transmission optimization includes data compensation and adding redundant sensors, and data compensation is performed through linear interpolation; data transmission optimization includes optimizing communication protocols, adding backup communication paths and data compression; sensor deployment optimization includes optimizing sensor layout and increasing coverage density.

[0059] In this embodiment, the preset integrity threshold is represented by the maximum value of the integrity assessment value in the historical time period; data compensation is performed by calculating the compensation value through linear interpolation of valid data points before and after the data missing time window to complete the lost data; a redundant sensor is deployed in key areas to ensure that when a sensor fails or the data is incomplete, other sensors can make up for the missing data; redundant sensors are configured to improve the robustness of the device and the reliability of the data.

[0060] The Message Queuing Telemetry Transport (MQTT) protocol is used to optimize the communication protocol to optimize the data transmission path and bandwidth management to ensure that data packet loss during transmission is minimized; a backup communication path is configured, and when the main path fails, it can automatically switch to the backup path to ensure the continuity and reliability of data transmission; the backup communication path is based on cellular networks, wireless fidelity (Wireless Fidelity, Wi-Fi), long-distance communication technology (Long Range, LoRa) or satellite communication and other methods; the transmitted data is compressed through data compression algorithms (such as gzip) to reduce the data transmission load and improve transmission efficiency; through the above steps, the integrity, accuracy and stability of data in carbon emission monitoring are improved.

[0061] Furthermore, sensor deployment optimization includes optimizing sensor layout and increasing coverage density; optimizing sensor layout identifies data blind spots through map analysis tools and adds sensors in data blind spots, where data blind spots represent areas not covered by sensor signals; increasing coverage density means adding gas analyzers in areas where the number of sensor coverage is less than the preset number of sensors and increasing the number of sensors in key areas according to seasonal changes, where key areas represent areas where carbon emission concentrations are greater than preset concentrations.

[0062] In this embodiment, the number of preset sensors is set according to the importance of the area. For example, an industrial area is set to have at least 3-4 gas analyzers per square kilometer; changes in climate and seasons may cause fluctuations in carbon emissions. For example, the winter heating season or the summer high temperature season may cause a significant increase in carbon emissions. Therefore, monitoring is strengthened by increasing the number of gas analyzers during the winter heating season or the summer high temperature season; the preset concentration is set according to industry standards. For example, in industrial areas, the preset concentration is set to 1000ppm; through the above steps, the reasonable layout of the gas analyzers is ensured, thereby improving the coverage and data collection accuracy of the gas analyzers, thereby achieving improved data accuracy in carbon emission monitoring.

[0063] Further, the specific method for performing data accuracy evaluation based on the acquired second carbon emission monitoring data to obtain an accuracy evaluation value is as follows: the sensor drift rate deviation is obtained based on the sensor drift rate and the preset sensor drift rate in the database (i.e., the specific method for obtaining the accuracy evaluation value is as follows: ), the sensor drift rate deviation is obtained by calculating the ratio of the sensor drift rate to the preset sensor drift rate; when the monitoring point temperature is greater than the preset monitoring point temperature maximum value in the database, the monitoring point temperature deviation is obtained according to the monitoring point temperature and the preset monitoring point temperature maximum value in the database (i.e., the specific method of obtaining the accuracy evaluation value). ), the temperature deviation of the monitoring point is obtained by calculating the ratio of the temperature of the monitoring point to the preset maximum temperature of the monitoring point, otherwise the temperature deviation of the monitoring point is recorded as 0; the sensor drift rate deviation, noise deviation, monitoring point wind speed deviation and monitoring point temperature deviation are processed to obtain the accuracy evaluation value, the noise deviation (i.e., the specific method of obtaining the accuracy evaluation value) ) is obtained by calculating the ratio of the noise to the preset noise, and the wind speed deviation at the monitoring point (i.e., the specific method for obtaining the accuracy evaluation value) is ) is obtained by calculating the ratio of the wind speed at the monitoring point to the wind speed at the preset monitoring point; the specific method for obtaining the accuracy evaluation value is:

[0064]

[0065] In the formula, ZQ represents the accuracy evaluation value, represents the optimized integrity assessment value, PYI represents the sensor drift rate, ZSH represents the noise, WDP represents the temperature deviation of the monitoring point, FSU represents the wind speed of the monitoring point, WD represents the temperature of the monitoring point, Indicates the preset sensor drift rate, represents the preset noise, Indicates the wind speed at the preset monitoring point. Indicates the maximum temperature of the preset monitoring point. Indicates the minimum temperature of the preset monitoring point.

[0066] In this embodiment, the preset sensor drift rate is represented by the average value of the sensor drift rate in the historical time period; the preset noise is represented by the average value of the noise in the historical time period; the preset monitoring point wind speed is represented by the average value of the monitoring point wind speed in the historical time period; the preset monitoring point temperature maximum value is set according to the specific performance of the gas analyzer, for example, if the maximum withstand temperature of the gas analyzer currently in use is 50°C, then the preset monitoring point temperature maximum value is set to 50°C; the preset monitoring point temperature minimum value is represented according to the minimum value of the monitoring point temperature in the historical database.

[0067] The accuracy evaluation value in this algorithm involves processing multiple independent variables (sensor drift rate, noise, monitoring point temperature and monitoring point wind speed), and there is a mutual influence relationship between these independent variables; the existence of sensor drift rate may aggravate the influence of noise, therefore, the larger the sensor drift rate, the greater the noise; temperature change is one of the main reasons for sensor zero drift, as the temperature deviation increases, the internal structure and component parameters of the sensor may change, resulting in the drift of the output signal, the larger the sensor drift rate; high temperature may aggravate the electromagnetic interference problem and increase the current noise level in electronic devices, the larger the temperature deviation, the greater the noise; when the wind speed is higher, the amount of gas sample collected may be uneven, thereby increasing the noise in the measurement; when the wind speed is high, the diffusion rate of gas in the air increases, the larger the sensor drift rate; therefore, the accuracy evaluation value decreases with the increase of sensor drift rate, noise, temperature deviation and wind speed.

[0068] Taking the optimized integrity assessment value of 0.458, the preset sensor drift rate of 0.1%, the preset noise of 20ppm, the preset monitoring point wind speed of 5m / s, the preset monitoring point temperature maximum value of 50℃, and the preset monitoring point temperature minimum value of 10℃ as an example, the change statistics of the accuracy assessment value are shown in Table 1:

[0069]

[0070] It can be seen from the first and second groups of data in the table that when the noise, monitoring point temperature and monitoring point wind speed remain unchanged, the accuracy evaluation value decreases with the increase of the sensor drift rate; it can be seen from the second and third groups of data that when the sensor drift rate, monitoring point temperature and monitoring point wind speed remain unchanged, the accuracy evaluation value decreases with the increase of the noise; it can be seen from the third and fourth groups of data that when the sensor drift rate, monitoring point temperature and noise remain unchanged, the accuracy evaluation value decreases with the increase of the monitoring point wind speed; it can be seen from the fifth and sixth groups of data that when the sensor drift rate, monitoring point wind speed and noise remain unchanged, and the monitoring point temperature is greater than the preset monitoring point temperature maximum value, the accuracy evaluation value decreases with the increase of the monitoring point temperature; it can be seen from the fourth and seventh groups of data that when the sensor drift rate, monitoring point wind speed and noise remain unchanged, and the monitoring point temperature is not greater than the preset monitoring point temperature maximum value, the accuracy evaluation value does not change with the change of the monitoring point temperature.

[0071] Through the above steps, the accuracy of the data is quantitatively evaluated, thereby achieving improved accuracy of carbon emission monitoring data.

[0072] Furthermore, the specific process of determining whether to perform data accuracy optimization based on the accuracy evaluation value is as follows: determine whether the accuracy evaluation value is less than a preset accuracy threshold in the database. If the accuracy evaluation value is less than the preset accuracy threshold, perform sensor optimization, otherwise do not perform sensor optimization. When the accuracy evaluation value obtained after sensor optimization is less than the preset accuracy threshold in the database, perform data smoothing, otherwise terminate data accuracy optimization. Sensor optimization includes calibrating sensors and replacing sensors.

[0073] In this embodiment, the preset accuracy threshold is represented by the maximum value of the accuracy evaluation value in the historical time period; the calibration is usually performed by adjusting the output value of the sensor or calibrating the sensor with a standard gas to ensure that its measurement accuracy is improved; if the accuracy evaluation value after calibration is still less than the preset accuracy threshold, or the sensor itself is damaged or aged, the sensor may need to be replaced, and a new sensor should be selected during the replacement process to ensure that its performance meets the standard; data smoothing is performed by the moving average method; through the above steps, errors caused by factors such as sensors and environmental changes are reduced, making the data more stable and reliable, thereby achieving improved accuracy of carbon emission monitoring data.

[0074] Further, the specific method for evaluating the optimized evaluation value according to the sensor accuracy, the integrity evaluation value obtained after optimization, and the accuracy evaluation value obtained after optimization is as follows: the sensor accuracy deviation is obtained according to the sensor accuracy and the preset sensor accuracy in the database (i.e., the value in the specific method for obtaining the optimized evaluation value). ), the sensor accuracy deviation is obtained by processing the ratio of the sensor accuracy and the preset sensor accuracy; the accuracy optimization coefficient (i.e., the specific method of obtaining the optimized evaluation value) is obtained according to the optimized accuracy evaluation value, the accuracy evaluation value and the preset accuracy threshold in the database. ), the accuracy optimization coefficient is obtained by processing the ratio of the difference between the optimized accuracy evaluation value and the accuracy evaluation value and the preset accuracy threshold; the integrity optimization coefficient is obtained according to the optimized integrity evaluation value, the integrity evaluation value and the preset integrity threshold in the database (that is, the specific method of obtaining the optimized evaluation value ), the integrity optimization coefficient is obtained by processing the ratio of the difference between the optimized integrity assessment value and the integrity assessment value and the preset integrity threshold; the sensor precision deviation, the accuracy optimization coefficient and the integrity optimization coefficient are processed to obtain the optimized assessment value.

[0075] Among them, the specific method of obtaining the optimized evaluation value is:

[0076]

[0077] In the formula, YH represents the optimized evaluation value, represents the sensor accuracy after optimization of the i-th sensor, represents the sensor accuracy of the i-th sensor, represents the accuracy evaluation value after optimization, ZQ represents the accuracy evaluation value, represents the optimized integrity assessment value, WZ represents the integrity assessment value, represents the preset accuracy threshold, Indicates the preset integrity threshold.

[0078] In this embodiment, the optimization evaluation value in this algorithm involves processing multiple independent variables (sensor precision deviation, accuracy optimization coefficient and integrity optimization coefficient), and there is a mutual influence relationship between these independent variables; the smaller the sensor precision deviation, the smaller the difference between its measured value and the true value, and therefore the higher the accuracy optimization coefficient; if the sensor frequently malfunctions or fails due to excessive precision deviation, its integrity may be affected, resulting in a decrease in the integrity optimization coefficient; reducing precision deviation and improving accuracy will directly improve the data quality of the sensor, thereby increasing the integrity optimization coefficient; in summary, the optimization evaluation value is positively correlated with the sensor precision deviation, the accuracy optimization coefficient and the integrity optimization coefficient.

[0079] Through the above steps, the degree to which the optimization measures improve data quality is quantitatively evaluated, thereby achieving improved accuracy in carbon emission monitoring.

[0080] Furthermore, the specific process for determining whether to provide feedback based on the optimization evaluation value is as follows: determine whether the optimization evaluation value is less than the preset optimization threshold in the database: if the optimization evaluation value is less than the preset optimization threshold in the database, environmental compensation is performed; if the optimization evaluation value is not less than the preset optimization threshold in the database, no feedback is performed; environmental compensation means real-time monitoring of the temperature and wind speed of the monitoring point, and compensation is performed based on the impact of the temperature and wind speed of the monitoring point on the sensor; if the optimization evaluation value obtained after environmental compensation is less than the preset optimization threshold in the database, feedback is performed, otherwise the feedback is terminated.

[0081] In this embodiment, the preset optimization threshold is represented by the maximum value of the optimization evaluation value in the historical time period; and the feedback represents sending the optimization evaluation value to the preset personnel.

[0082] Compensate for the impact of the monitoring point temperature and the monitoring point wind speed on the sensor: construct a mapping set of environmental data and its corresponding influencing factors based on the relationship between historical environmental data (monitoring point temperature and monitoring point wind speed) and the integrity assessment value, and input the real-time environmental data into the mapping set to obtain the corresponding first environmental influencing factor; construct a mapping set of environmental data and its corresponding influencing factors based on the relationship between historical environmental data (monitoring point temperature and monitoring point wind speed) and the accuracy assessment value, and input the real-time environmental data into the mapping set to obtain the corresponding second environmental influencing factor.

[0083] Specifically, the first environmental impact factor is added to the integrity assessment value formula, and the integrity assessment formula after environmental compensation is: ,in, represents the integrity assessment after environmental compensation, represents the first environmental impact factor; the second environmental impact factor is added to the accuracy evaluation value formula, and the accuracy evaluation formula after environmental compensation is: ,in, represents the accuracy evaluation value after environmental compensation, Represents the second environmental impact factor; after that, if the obtained optimization evaluation value is not less than the preset optimization threshold in the database, the signal output by the sensor is corrected in real time through the environmental compensation technology. For example, in air quality monitoring, a sensor is used to monitor the concentration of particulate matter in the air. However, due to changes in environmental factors such as temperature and humidity, the output of the sensor may produce errors. In order to correct these errors, the monitoring station can adopt environmental compensation technology, which monitors the changes in environmental factors in real time and corrects the output of the sensor in real time based on the known sensor characteristics and the influence of environmental factors on the output, so as to obtain more accurate particulate matter concentration data.

[0084] Through the above steps, the sensor errors caused by temperature and wind speed can be corrected, and the measurement errors and data fluctuations caused by environmental factors can be reduced, thereby improving the accuracy and completeness of the data obtained by the sensor, and further achieving improved accuracy in carbon emission monitoring.

[0085] like Figure 6As shown, it is a structural diagram of a carbon emission monitoring device provided in an embodiment of the present application. A carbon emission monitoring device provided in an embodiment of the present application includes: a data integrity assessment module, a data accuracy assessment module and an optimization assessment module: wherein, the data integrity assessment module: is used to perform data integrity assessment according to the acquired first carbon emission monitoring data to obtain an integrity assessment value, and judge whether to perform data integrity optimization based on the integrity assessment value, the first carbon emission monitoring data is used to evaluate the integrity of the carbon emission monitoring data, and the integrity assessment value is used to evaluate the integrity of the carbon emission monitoring data; the data accuracy assessment module: is used to perform data accuracy assessment according to the acquired second carbon emission monitoring data to obtain an accuracy assessment value, and judge whether to perform data accuracy optimization based on the accuracy assessment value, the second carbon emission monitoring data is used to evaluate the accuracy of the carbon emission monitoring data, and the accuracy assessment value is used to evaluate the accuracy of the carbon emission monitoring data; the optimization assessment module: is used to perform assessment according to the sensor accuracy, the integrity assessment value obtained after optimization and the accuracy assessment value obtained after optimization to obtain an optimization assessment value, and judge whether to provide feedback based on the optimization assessment value, and the optimization assessment value is used to evaluate the effectiveness of the optimization measures.

[0086] In this embodiment, through data integrity assessment and data accuracy assessment, the quality of carbon emission monitoring data is improved, providing a reliable basis for subsequent carbon emission analysis and decision-making; by optimizing the evaluation module, timely discovery and optimization of problems in the monitoring process are achieved, the monitoring efficiency is improved, and the data accuracy of carbon emission monitoring is improved.

[0087] To summarize, the embodiment of the present application determines whether to perform data integrity optimization based on the integrity assessment value obtained by the first carbon emission monitoring data obtained, and then determines whether to perform data accuracy optimization based on the accuracy assessment value obtained by the second carbon emission monitoring data obtained, and finally determines whether to provide feedback based on the optimized assessment value obtained based on the sensor accuracy, the optimized integrity assessment value and the optimized accuracy assessment value, thereby improving the integrity of the carbon emission monitoring data, and further improving the data accuracy of carbon emission monitoring, effectively solving the problem of low data accuracy of carbon emission monitoring in the prior art.

[0088] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0089] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0090] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0091] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0092] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0093] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A carbon emission monitoring method, characterized in that: The following steps are involved: S1, performing data integrity assessment according to the acquired first carbon emission monitoring data to obtain an integrity assessment value, and judging whether to perform data integrity optimization based on the integrity assessment value, wherein the first carbon emission monitoring data is used to assess the integrity of the carbon emission monitoring data, and the integrity assessment value is used to assess the integrity of the carbon emission monitoring data; S2, performing data accuracy assessment according to the acquired second carbon emission monitoring data to obtain an accuracy assessment value, and judging whether to perform data accuracy optimization based on the accuracy assessment value, wherein the second carbon emission monitoring data is used to assess the accuracy of the carbon emission monitoring data, and the accuracy assessment value is used to assess the accuracy of the carbon emission monitoring data; S3, evaluating the sensor accuracy, the integrity evaluation value obtained after optimization, and the accuracy evaluation value obtained after optimization to obtain an optimized evaluation value, and judging whether to provide feedback based on the optimized evaluation value, wherein the optimized evaluation value is used to evaluate the effectiveness of the optimization measures; The first carbon emission monitoring data includes sensor failure rate, data loss rate, sensor accuracy, sensor response time, deployment density, coverage, and key emission source identification rate; The second carbon emission monitoring data includes sensor drift rate, noise, monitoring point temperature and monitoring point wind speed; The sensor failure rate deviation, sensor deployment density deviation, sensor coverage deviation and key emission source identification rate deviation are processed to obtain the sensor deployment coefficient; The network stability factor, sensor performance coefficient and sensor deployment coefficient in the database are processed to obtain the integrity assessment value; The sensor drift rate deviation, noise deviation, monitoring point wind speed deviation and monitoring point temperature deviation are processed to obtain an accuracy evaluation value. The noise deviation is obtained by processing the ratio of the noise to the preset noise, and the monitoring point wind speed deviation is obtained by processing the ratio of the monitoring point wind speed to the preset monitoring point wind speed.

2. A carbon emission monitoring method as claimed in claim 1, characterized in that: The sensor failure rate deviation is obtained according to the sensor failure rate and the preset sensor failure rate in the database; The data loss rate deviation is obtained according to the data loss rate and the preset data loss rate in the database; The sensor response time deviation is obtained according to the sensor response time and the preset sensor response time in the database; The sensor accuracy deviation is obtained according to the sensor accuracy and the preset sensor accuracy in the database; The sensor performance coefficient is obtained by processing the sensor failure rate deviation, the data loss rate deviation, the sensor response time deviation and the sensor accuracy deviation; The sensor deployment density deviation is obtained according to the sensor deployment density and the preset sensor deployment density in the database; The sensor coverage deviation is obtained according to the sensor coverage and the preset sensor coverage in the database; The key emission source identification rate deviation is obtained according to the key emission source identification rate and the preset key emission source identification rate in the database.

3. A carbon emission monitoring method as claimed in claim 2, characterized in that: The specific process of determining whether to perform data integrity optimization based on the integrity evaluation value is as follows: Determine whether the integrity evaluation value is less than a preset integrity threshold in the database. If the integrity evaluation value is less than the preset integrity threshold in the database, perform data integrity optimization; otherwise, do not perform data integrity optimization. The data integrity optimization includes sensor performance optimization, data transmission optimization and sensor deployment optimization; The data transmission optimization includes data compensation and adding redundant sensors; The sensor deployment optimization includes optimizing sensor layout and increasing coverage density.

4. A carbon emission monitoring method as claimed in claim 3, characterized in that: The sensor deployment optimization includes optimizing sensor layout and increasing coverage density; The optimizing sensor layout identifies data blind spots through map analysis tools and adds sensors in the data blind spots; Increasing the coverage density means adding gas analyzers in areas where the number of sensors covered is less than the preset number of sensors and increasing the number of sensors in key areas according to seasonal changes.

5. A carbon emission monitoring method as claimed in claim 1, characterized in that: The sensor drift rate deviation is obtained according to the sensor drift rate and a preset sensor drift rate in a database; The method for obtaining the temperature deviation of the monitoring point is: When the temperature of the monitoring point is greater than the maximum temperature of the preset monitoring point in the database, the temperature deviation of the monitoring point is obtained according to the temperature of the monitoring point and the maximum temperature of the preset monitoring point in the database, otherwise the temperature deviation of the monitoring point is recorded as 0; The specific method to obtain the accuracy evaluation value is: In the formula, ZQ represents the accuracy evaluation value, represents the optimized integrity assessment value, PYI represents the sensor drift rate, ZSH represents the noise, WDP represents the temperature deviation of the monitoring point, FSU represents the wind speed of the monitoring point, Indicates the preset sensor drift rate, represents the preset noise, Indicates the wind speed at the preset monitoring point.

6. A carbon emission monitoring method as claimed in claim 5, characterized in that: The specific process of determining whether to optimize data accuracy based on the accuracy evaluation value is as follows: Determine whether the accuracy evaluation value is less than a preset accuracy threshold in the database, if the accuracy evaluation value is less than the preset accuracy threshold, perform sensor optimization, otherwise do not perform sensor optimization; When the accuracy evaluation value obtained after sensor optimization is less than the preset accuracy threshold in the database, data smoothing is performed, otherwise the data accuracy optimization is terminated. The sensor optimization includes calibrating the sensor and replacing the sensor.

7. A carbon emission monitoring method as claimed in claim 1, characterized in that: The specific method of evaluating the optimized evaluation value according to the sensor accuracy, the integrity evaluation value obtained after optimization, and the accuracy evaluation value obtained after optimization to obtain the optimized evaluation value is as follows: Obtaining a sensor accuracy deviation according to the sensor accuracy and a preset sensor accuracy in a database; Obtaining an accuracy optimization coefficient according to the optimized accuracy evaluation value, the accuracy evaluation value, and a preset accuracy threshold in a database; Obtaining an integrity optimization coefficient according to the optimized integrity assessment value, the integrity assessment value, and a preset integrity threshold in a database; The sensor precision deviation, accuracy optimization coefficient and integrity optimization coefficient are processed to obtain the optimized evaluation value.

8. A carbon emission monitoring method as claimed in claim 7, characterized in that: The specific process of judging whether to provide feedback based on the optimization evaluation value is as follows: Determine whether the optimization evaluation value is less than the preset optimization threshold in the database: If the optimization evaluation value is less than the preset optimization threshold in the database, environmental compensation is performed; If the optimization evaluation value is not less than the preset optimization threshold in the database, no feedback is given; If the optimization evaluation value obtained after environmental compensation is less than the preset optimization threshold in the database, feedback is performed, otherwise the feedback is terminated.

9. A carbon emission monitoring device, characterized in that: Including data integrity assessment module, data accuracy assessment module and optimization assessment module: The data integrity assessment module is used to perform data integrity assessment according to the acquired first carbon emission monitoring data to obtain an integrity assessment value, and determine whether to perform data integrity optimization based on the integrity assessment value, wherein the first carbon emission monitoring data is used to assess the integrity of the carbon emission monitoring data, and the integrity assessment value is used to assess the integrity of the carbon emission monitoring data; The data accuracy assessment module is used to perform data accuracy assessment based on the acquired second carbon emission monitoring data to obtain an accuracy assessment value, and determine whether to perform data accuracy optimization based on the accuracy assessment value, wherein the second carbon emission monitoring data is used to assess the accuracy of the carbon emission monitoring data, and the accuracy assessment value is used to assess the accuracy of the carbon emission monitoring data; The optimization evaluation module is used to evaluate the sensor accuracy, the integrity evaluation value obtained after optimization, and the accuracy evaluation value obtained after optimization to obtain an optimization evaluation value, and determine whether to provide feedback based on the optimization evaluation value, wherein the optimization evaluation value is used to evaluate the effectiveness of the optimization measures; The first carbon emission monitoring data includes sensor failure rate, data loss rate, sensor accuracy, sensor response time, deployment density, coverage, and key emission source identification rate; The second carbon emission monitoring data includes sensor drift rate, noise, monitoring point temperature and monitoring point wind speed; The sensor failure rate deviation, sensor deployment density deviation, sensor coverage deviation and key emission source identification rate deviation are processed to obtain the sensor deployment coefficient; The network stability factor, sensor performance coefficient and sensor deployment coefficient in the database are processed to obtain the integrity assessment value; The sensor drift rate deviation, noise deviation, monitoring point wind speed deviation and monitoring point temperature deviation are processed to obtain an accuracy evaluation value. The noise deviation is obtained by processing the ratio of the noise to the preset noise, and the monitoring point wind speed deviation is obtained by processing the ratio of the monitoring point wind speed to the preset monitoring point wind speed.

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