Carbon emission monitoring method and system

Abstract

The invention relates to the technical field of carbon emission monitoring, and discloses a carbon emission monitoring method and system, and the method comprises the steps: 1, deploying various types of sensors in a target region, including a carbon dioxide concentration sensor, a temperature sensor and a humidity sensor, for sensing environmental parameters in real time, and transmitting the environmental parameters to a server; the number and the positions of the sensors are optimally designed according to geographic features and emission source distribution of a target area; step 2, acquiring environmental parameter data output by each sensor in real time by using an Internet of Things technology; according to the invention, by optimizing sensor arrangement and a data processing algorithm, high-precision monitoring of carbon emission is realized; by utilizing the Internet of Things technology and big data processing capability, acquisition, processing and display of a large amount of data can be completed in a short time, and the requirement of real-time monitoring is met; and by setting the early warning threshold value, the user can be reminded in time when the carbon emission is abnormal, so that problems can be found in time and measures can be taken.

Description

Technical Field

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

[0002] As the global climate change problem becomes increasingly serious, carbon emission monitoring has become an important part of environmental protection and sustainable development. With the rapid development of big data, intelligence, cloud computing, and Internet technologies, as well as the urgent need to quantify carbon emission data, the collection, quantification, statistics, and analysis of corporate carbon emission information have received increasing attention. However, the current way of collecting corporate carbon emission information is still relatively traditional, and carbon emission data has not been fully collected and effectively utilized. Traditional carbon emission monitoring methods usually rely on regular sampling and laboratory analysis, which is not only time-consuming and labor-intensive, but also difficult to achieve real-time monitoring and large-scale coverage. For this reason, a carbon emission monitoring method and system are proposed. Summary of the invention

[0003] The purpose of the present invention is to provide a carbon emission monitoring method and system to solve the problem mentioned in the above background technology that traditional carbon emission monitoring methods usually rely on periodic sampling and laboratory analysis, which is not only time-consuming and labor-intensive, but also difficult to achieve real-time monitoring and large-scale coverage.

[0004] To achieve the above object, the present invention provides the following technical solution: a carbon emission monitoring method, comprising:

[0005] Step 1: Deploy various types of sensors in the target area, including carbon dioxide concentration sensors, temperature sensors, and humidity sensors, to sense environmental parameters in real time. The number and location of sensors are optimized based on the geographical characteristics of the target area and the distribution of emission sources.

[0006] Step 2: Use the Internet of Things technology to collect environmental parameter data output by each sensor in real time;

[0007] Step 3: Perform preprocessing operations such as filtering, denoising and baseline correction on the collected environmental parameter data, and then use machine learning algorithms to perform data fusion analysis to extract key features related to carbon emissions;

[0008] Step 4: Calculate carbon emissions using a specific mathematical model based on the extracted key features. The calculation formula is:

[0009]

[0010] Among them, E is the total carbon emissions, C i is the carbon dioxide concentration measured by the i-th sensor, F i is the emission factor corresponding to the i-th sensor, and n is the number of sensors;

[0011] Step 5: The calculated carbon emissions are intuitively displayed to the user in graphical or numerical form, and an early warning threshold is set. When the carbon emissions exceed the preset threshold, the system automatically triggers the early warning mechanism to remind the user to take corresponding measures.

[0012] Preferably, in step 3, the machine learning algorithm formula is as follows:

[0013]

[0014] in, is the predicted value, N is the number of decision trees, T i (x) is the prediction result of the i-th decision tree.

[0015] Preferably, in step 4, the emission factor F i It is determined based on the specific conditions of the sensor location, and the emission factor can be obtained through regression analysis of historical data.

[0016] Preferably, in step 4, a statistical method is used to detect outliers in the calculated data, and the formula is:

[0017]

[0018] Among them, X is the observed value, μ is the mean value, σ is the standard deviation, and if |Z|>3, the value is considered to be an outlier.

[0019] Preferably, in step 4, the abnormal data is corrected by adopting a correction method to ensure that the calculation of carbon emissions is not affected by individual erroneous data.

[0020] Preferably, the correction method mentioned above includes a replacement method, an interpolation method and a prediction model;

[0021] Replacement method: replace outliers with the median or mean;

[0022] Interpolation method: use linear interpolation, polynomial interpolation and other methods to estimate outliers;

[0023] Predictive model: Use historical data to train predictive models to predict and correct outliers.

[0024] A carbon emission monitoring system, comprising a sensor arrangement module, a data acquisition module, a data processing module, a carbon emission calculation module and a data visualization and early warning module;

[0025] The sensor placement module is used to deploy multiple types of sensors in the target area;

[0026] The data acquisition module is used to collect environmental parameter data output by each sensor in real time using the Internet of Things technology;

[0027] The data processing module is used to perform pre-processing operations such as filtering, denoising and baseline correction on the collected environmental parameter data, and then use a machine learning algorithm to perform fusion analysis on the data to extract key features related to carbon emissions. The machine learning algorithm formula is as follows:

[0028]

[0029] in, is the predicted value, N is the number of decision trees, T i (x) is the prediction result of the i-th decision tree;

[0030] The carbon emission calculation module uses a specific mathematical model to calculate carbon emissions, and the calculation formula is:

[0031]

[0032] Among them, E is the total carbon emissions, C i is the carbon dioxide concentration measured by the i-th sensor, F i is the emission factor corresponding to the i-th sensor, and n is the number of sensors;

[0033] The data visualization and early warning module is used to intuitively display the calculated carbon emissions to the user in the form of graphics or numerical values, and to set early warning thresholds.

[0034] Preferably, the sensor arrangement module is connected to the data acquisition module, the data acquisition module is connected to the data processing module, the data processing module is connected to the carbon emission calculation module, and the carbon emission calculation module is connected to the data visualization and early warning module.

[0035] Preferably, the carbon emission calculation module comprises an anomaly detection unit and a data correction unit;

[0036] The anomaly detection unit is used to detect abnormal values ​​in the calculation data, and the formula is:

[0037]

[0038] Where X is the observed value, μ is the mean value, σ is the standard deviation, and if |Z|>3, the value is considered an outlier;

[0039] The data correction unit is used to correct the detected abnormal value by using a correction method;

[0040] The correction methods include substitution method, interpolation method and prediction model;

[0041] Replacement method: replace outliers with the median or mean;

[0042] Interpolation method: use linear interpolation, polynomial interpolation and other methods to estimate outliers;

[0043] Predictive model: Use historical data to train predictive models to predict and correct outliers.

[0044] Compared with the prior art, the present invention adopts the above technical scheme and has the following technical effects: the present invention realizes high-precision monitoring of carbon emissions by optimizing the sensor layout and data processing algorithm; by utilizing the Internet of Things technology and big data processing capabilities, it can complete the collection, processing and display of a large amount of data in a short time, meeting the needs of real-time monitoring; by setting an early warning threshold, it can promptly remind users when the carbon emissions are abnormal, which helps to promptly discover problems and take measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 It is a system schematic diagram of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the conditions under which this application can be implemented. Therefore, they have no substantive technical significance. Any structural modification, change in proportional relationship or adjustment of size should still fall within the scope of the technical content disclosed in this application without affecting the effects and purposes that can be achieved by this application.

[0049] Example

[0050] See also Figure 1 The present invention provides a technical solution: a carbon emission monitoring method, comprising:

[0051] Step 1: Deploy various types of sensors in the target area, including carbon dioxide concentration sensors, temperature sensors, and humidity sensors, to sense environmental parameters in real time. The number and location of sensors are optimized based on the geographical characteristics of the target area and the distribution of emission sources.

[0052] Step 2: Use the Internet of Things technology to collect environmental parameter data output by each sensor in real time. The collection system has high sampling rate and high-precision analog-to-digital conversion capabilities to ensure the integrity and accuracy of the data;

[0053] Step 3: Perform preprocessing operations such as filtering, denoising and baseline correction on the collected environmental parameter data to improve data quality, and then use machine learning algorithms to perform data fusion analysis to extract key features related to carbon emissions;

[0054] Step 4: Calculate carbon emissions using a specific mathematical model based on the extracted key features. The calculation formula is:

[0055]

[0056] Among them, E is the total carbon emissions, C i is the carbon dioxide concentration measured by the i-th sensor, F i is the emission factor corresponding to the i-th sensor, and n is the number of sensors;

[0057] Step 5: The calculated carbon emissions are intuitively displayed to the user in graphical or numerical form, and an early warning threshold is set. When the carbon emissions exceed the preset threshold, the system automatically triggers the early warning mechanism to remind the user to take corresponding measures.

[0058] In step 3, the machine learning algorithm formula is as follows:

[0059]

[0060] in, is the predicted value, N is the number of decision trees, T i (x) is the prediction result of the i-th decision tree.

[0061] In Step 4, the emission factor F i It is determined based on the specific conditions of the sensor's location and may be affected by a variety of factors, such as energy type, equipment efficiency, operating conditions, etc. The emission factor can be obtained through historical data regression analysis or determined based on industry standards or literature.

[0062] In step 4, statistical methods are used to detect outliers in the calculated data. The formula is:

[0063]

[0064] Among them, X is the observed value, μ is the mean value, σ is the standard deviation, and if |Z|>3, the value is considered to be an outlier.

[0065] In step 4, the abnormal data is corrected by adopting a correction method to ensure that the calculation of carbon emissions is not affected by individual erroneous data.

[0066] Correction methods include substitution, interpolation, and prediction models;

[0067] Replacement method: replace outliers with the median or mean;

[0068] Interpolation method: use linear interpolation, polynomial interpolation and other methods to estimate outliers;

[0069] Predictive model: Use historical data to train predictive models to predict and correct outliers.

[0070] A carbon emission monitoring system, comprising a sensor arrangement module, a data acquisition module, a data processing module, a carbon emission calculation module and a data visualization and early warning module;

[0071] The sensor placement module is used to deploy various types of sensors in the target area, including carbon dioxide concentration sensors, temperature sensors, and humidity sensors, for real-time perception of environmental parameters. The number and location of sensors are optimized according to the geographical characteristics of the target area and the distribution of emission sources.

[0072] The data acquisition module is used to collect environmental parameter data output by each sensor in real time using the Internet of Things technology. The acquisition system has high sampling rate and high-precision analog-to-digital conversion capabilities to ensure the integrity and accuracy of the data;

[0073] The data processing module is used to perform preprocessing operations such as filtering, denoising and baseline correction on the collected environmental parameter data, and then use the machine learning algorithm to fuse and analyze the data to extract key features related to carbon emissions. The machine learning algorithm formula is as follows:

[0074]

[0075] in, is the predicted value, N is the number of decision trees, T i (x) is the prediction result of the i-th decision tree;

[0076] The carbon emission calculation module uses a specific mathematical model to calculate carbon emissions. The calculation formula is:

[0077]

[0078] Among them, E is the total carbon emissions, C iis the carbon dioxide concentration measured by the i-th sensor, F i is the emission factor corresponding to the i-th sensor, and n is the number of sensors;

[0079] The data visualization and early warning module is used to intuitively display the calculated carbon emissions to users in the form of graphics or numerical values, and set early warning thresholds. When carbon emissions exceed the preset threshold, the system automatically triggers the early warning mechanism to remind users to take corresponding measures.

[0080] The sensor arrangement module is connected to the data acquisition module, the data acquisition module is connected to the data processing module, the data processing module is connected to the carbon emission calculation module, and the carbon emission calculation module is connected to the data visualization and early warning module.

[0081] The carbon emission calculation module includes an anomaly detection unit and a data correction unit;

[0082] The anomaly detection unit is used to detect abnormal values ​​in the calculation data. The formula is:

[0083]

[0084] Where X is the observed value, μ is the mean value, σ is the standard deviation, and if |Z|>3, the value is considered an outlier;

[0085] A data correction unit, used for correcting the detected abnormal value by a correction method;

[0086] Correction methods include substitution, interpolation, and prediction models;

[0087] Replacement method: replace outliers with the median or mean;

[0088] Interpolation method: use linear interpolation, polynomial interpolation and other methods to estimate outliers;

[0089] Predictive model: Use historical data to train predictive models to predict and correct outliers.

[0090] In summary, by optimizing the sensor layout and data processing algorithms, high-precision monitoring of carbon emissions is achieved; by utilizing Internet of Things technology and big data processing capabilities, a large amount of data can be collected, processed and displayed in a short time, meeting the needs of real-time monitoring; by setting early warning thresholds, users can be reminded in time when carbon emissions are abnormal, which helps to detect problems and take measures in time.

[0091] It will be appreciated by those skilled in the art that the features described in the various embodiments and / or claims of the present invention may be combined and / or combined in various ways, even if such combinations and / or combinations are not explicitly described in the present invention. In particular, the features described in the various embodiments and / or claims of the present invention may be combined and / or combined in various ways without departing from the spirit and teachings of the present invention. All of these combinations and / or combinations fall within the scope of the present invention.

Claims

1. A carbon emission monitoring method, characterized in that: include Step 1: Deploy various types of sensors in the target area, including carbon dioxide concentration sensors, temperature sensors, and humidity sensors, to sense environmental parameters in real time. The number and location of sensors are optimized based on the geographical characteristics of the target area and the distribution of emission sources. Step 2: Use the Internet of Things technology to collect environmental parameter data output by each sensor in real time; Step 3: Perform preprocessing operations such as filtering, denoising and baseline correction on the collected environmental parameter data, and then use machine learning algorithms to perform data fusion analysis to extract key features related to carbon emissions; Step 4: Calculate carbon emissions using a specific mathematical model based on the extracted key features. The calculation formula is: Among them, E is the total carbon emissions, C i is the carbon dioxide concentration measured by the i-th sensor, F i is the emission factor corresponding to the i-th sensor, and n is the number of sensors; Step 5: The calculated carbon emissions are intuitively displayed to the user in graphical or numerical form, and an early warning threshold is set. When the carbon emissions exceed the preset threshold, the system automatically triggers the early warning mechanism to remind the user to take corresponding measures.

2. A carbon emission monitoring method according to claim 1, characterized in that: In step 3, the machine learning algorithm formula is as follows: in, is the predicted value, N is the number of decision trees, T i (x) is the prediction result of the i-th decision tree.

3. A carbon emission monitoring method according to claim 1, characterized in that: In Step 4, the emission factor F i It is determined based on the specific conditions of the sensor location, and the emission factor can be obtained through regression analysis of historical data.

4. A carbon emission monitoring method according to claim 1, characterized in that: In step 4, statistical methods are used to detect outliers in the calculated data. The formula is: Among them, X is the observed value, μ is the mean value, σ is the standard deviation, and if |Z|>3, the value is considered to be an outlier.

5. A carbon emission monitoring method according to claim 1, characterized in that: In step 4, the abnormal data is corrected by adopting a correction method to ensure that the calculation of carbon emissions is not affected by individual erroneous data.

6. A carbon emission monitoring method according to claim 5, characterized in that: The correction methods include substitution method, interpolation method and prediction model; Replacement method: replace outliers with the median or mean; Interpolation method: use linear interpolation, polynomial interpolation and other methods to estimate outliers; Predictive model: Use historical data to train predictive models to predict and correct outliers.

7. A system for monitoring carbon emissions, characterized in that: It includes sensor arrangement module, data acquisition module, data processing module, carbon emission calculation module and data visualization and early warning module; The sensor placement module is used to deploy multiple types of sensors in the target area; The data acquisition module is used to collect environmental parameter data output by each sensor in real time using the Internet of Things technology; The data processing module is used to perform pre-processing operations such as filtering, denoising and baseline correction on the collected environmental parameter data, and then use a machine learning algorithm to perform fusion analysis on the data to extract key features related to carbon emissions. The machine learning algorithm formula is as follows: in, is the predicted value, N is the number of decision trees, T i (x) is the prediction result of the i-th decision tree; The carbon emission calculation module uses a specific mathematical model to calculate carbon emissions, and the calculation formula is: Among them, E is the total carbon emissions, C i is the carbon dioxide concentration measured by the i-th sensor, F i is the emission factor corresponding to the i-th sensor, and n is the number of sensors; The data visualization and early warning module is used to intuitively display the calculated carbon emissions to the user in the form of graphics or numerical values, and to set early warning thresholds.

8. A carbon emission monitoring system according to claim 7, characterized in that: The sensor arrangement module is connected to the data acquisition module, the data acquisition module is connected to the data processing module, the data processing module is connected to the carbon emission calculation module, and the carbon emission calculation module is connected to the data visualization and early warning module.

9. A carbon emission monitoring system according to claim 7, characterized in that: The carbon emission calculation module includes an anomaly detection unit and a data correction unit; The anomaly detection unit is used to detect abnormal values ​​in the calculation data, and the formula is: Where X is the observed value, μ is the mean value, σ is the standard deviation, and if |Z|>3, the value is considered an outlier; The data correction unit is used to correct the detected abnormal value by using a correction method; The correction methods include substitution method, interpolation method and prediction model; Replacement method: replace outliers with the median or mean; Interpolation method: use linear interpolation, polynomial interpolation and other methods to estimate outliers; Predictive model: Use historical data to train predictive models to predict and correct outliers.

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