Emission reduction and carbon absorption monitoring system based on Internet of Things

Abstract

The invention discloses an emission reduction and carbon absorption monitoring system based on the Internet of Things, and relates to the field of environmental protection science and technology, the system comprises a monitoring center, the monitoring center is in communication connection with a data acquisition module, a data processing module, a data analysis module, a data management and control module and a data visualization module; the method comprises the following steps: dividing a monitoring area to obtain a plurality of monitoring sub-areas, and carrying out data acquisition on the monitoring sub-areas to obtain corresponding monitoring data; processing the collected monitoring data to obtain a corresponding carbon monitoring analysis model; analyzing the collected monitoring data according to the obtained carbon monitoring analysis model to obtain a corresponding early warning notice; according to the carbon emission and carbon absorption monitoring system, the corresponding emission reduction path is planned according to the obtained early warning information, the corresponding emission reduction path is verified, the corresponding early warning information is obtained and visually displayed, real-time, accurate and automatic monitoring of carbon emission and carbon absorption is achieved through the Internet of Things technology, and the monitoring efficiency and accuracy are improved.

Description

Technical Field

[0001] The present invention relates to the field of environmental protection technology, and in particular to an emission reduction and carbon absorption monitoring system based on the Internet of Things. Background Art

[0002] With the rapid development of industrialization and urbanization, the problem of carbon emissions has become increasingly serious, causing serious impacts on the environment and human health. Therefore, effective carbon emission monitoring and management is an important task at present; Compared with existing technologies, existing carbon emission monitoring systems mostly rely on manual sampling and laboratory analysis, which are inefficient and need to be improved in accuracy. However, most systems only focus on the monitoring of environmental parameters such as temperature and humidity, and the monitoring and management of carbon emissions have not been well solved. These are the problems we need to solve, so we provide an emission reduction and carbon absorption monitoring system based on the Internet of Things. Summary of the invention

[0003] The purpose of the present invention is to provide an emission reduction and carbon absorption monitoring system based on the Internet of Things.

[0004] The purpose of the present invention can be achieved through the following technical solutions: Compared with the prior art, the beneficial effects of the present invention are as follows: an emission reduction and carbon absorption monitoring system based on the Internet of Things, comprising a monitoring center, wherein the monitoring center is communicatively connected with a data acquisition module, a data processing module, a data analysis module, a data control module and a data visualization module; The data acquisition module is used to divide the monitoring area into several monitoring sub-areas, and collect data from the monitoring sub-areas to obtain corresponding monitoring data; The data processing module is used to process the collected monitoring data to obtain a corresponding carbon monitoring analysis model; The data analysis module is used to analyze the collected greenhouse data according to the obtained carbon monitoring analysis model to obtain corresponding early warning notifications; The data control module is used to plan the corresponding emission reduction path according to the obtained early warning information, and verify the corresponding emission reduction path to obtain the corresponding early warning information; The data visualization module is used to visualize the early warning information according to the carbon monitoring analysis model.

[0005] Furthermore, the data collection module divides the monitoring area into several monitoring sub-areas, and collects data from the monitoring sub-areas to obtain corresponding monitoring data, including: Obtain a plan view of the monitoring area, and divide the corresponding monitoring area into unequal parts according to the obtained plan view to obtain a number of monitoring sub-areas with unequal areas; Set up acquisition nodes to collect data from corresponding monitored sub - regions through the acquisition nodes, obtaining corresponding greenhouse data and operating parameters of carbon absorption equipment. The greenhouse data includes carbon emissions, temperature, humidity, wind speed, and greenhouse gas concentration; Collect data from corresponding monitored sub - regions through GIS technology to obtain corresponding geographical data. The geographical data includes vegetation coverage area, greening area, and carbon absorption equipment distribution data; Package the collected greenhouse data, operating parameters of carbon absorption equipment, and geographical data to obtain corresponding monitoring data, and upload the obtained monitoring data to the monitoring center for storage.

[0006] Further, the process by which the data processing module processes the collected monitoring data to obtain a corresponding carbon monitoring analysis model includes: Read the collected greenhouse data, construct a two - dimensional rectangular coordinate system with time regarding the greenhouse data, and generate corresponding greenhouse gas change curves according to the change amount of the greenhouse data. The greenhouse gas change curves include greenhouse gas concentration change curves, carbon emission data change curves, etc.; Obtain the operating parameters of the carbon absorption equipment from the monitoring center, input the obtained operating parameters, monitoring data, and geographical data into computer software, and establish a mathematical model based on the physical structure; According to the obtained greenhouse gas change curves, obtain several groups of data sets of greenhouse data, divide the obtained data sets of greenhouse data to obtain corresponding training sets and test sets, construct an initial prediction model based on a neural network, iteratively train the initial prediction model through the training set until the loss function training is stable, save the model parameters, and then verify the similarity of the output data matrix of the initial prediction model after iterative training through the training set. If the verification passes, output the corresponding prediction model; the prediction model is used to predict the carbon emissions and greenhouse gas concentration in the corresponding monitored sub - region i; Read the geographical data in the corresponding monitored sub - region i from the monitoring center, and input the read geographical data into 3D modeling software to obtain a corresponding digital twin model; Match the parameters of the obtained digital twin model, mathematical model, and prediction model to obtain a corresponding carbon monitoring analysis model.

[0007] Further, the process by which the data analysis module analyzes the collected greenhouse data according to the obtained carbon monitoring analysis model to obtain corresponding early warning notifications includes: Calculate the carbon absorption capacity in the corresponding monitored sub - region according to the collected greenhouse data to obtain a corresponding carbon absorption factor; Predict the greenhouse data in the corresponding monitoring sub-region based on the collected greenhouse data and in combination with the obtained carbon monitoring analysis model, obtain the corresponding prediction results, and obtain the corresponding carbon emission factors according to the prediction results; Obtain the corresponding first carbon neutralization coefficient based on the obtained carbon emission factors and carbon absorption factors, Record the obtained first carbon neutralization coefficient as TZ i ; Among them, ; Set the neutralization threshold tz; compare the obtained first carbon neutralization coefficient TZ i with the neutralization threshold tz; If TZ i ≤ tz, it indicates that the emission reduction and carbon absorption strategies in the corresponding monitoring sub-region i do not need to be adjusted, so no other operations are performed; If TZ i > tz, it indicates that the emission reduction and carbon absorption strategies in the corresponding monitoring sub-region i do not meet the requirements, then generate a warning notice and send it to the data control module.

[0008] Furthermore, the process by which the data control module plans the corresponding emission reduction path based on the obtained warning notice and verifies the corresponding emission reduction path to obtain the corresponding warning information includes: Plan the carbon emission path based on the obtained greenhouse data and carbon monitoring analysis model to obtain the corresponding first emission reduction path, and the first emission reduction path appropriately adjusts the energy consumption proportion in the corresponding monitoring region i; for example, increase the proportion of renewable energy consumption in the corresponding monitoring sub-region i; Re-plan in combination with the obtained first emission reduction path to obtain the corresponding second emission reduction path, and the second emission reduction path refers to optimizing or increasing the carbon absorption equipment in the corresponding monitoring sub-region i on the premise of energy conservation and emission reduction in the first emission reduction path; Adjust the parameters of the corresponding carbon monitoring analysis model according to the obtained emission reduction path and perform simulation operation to obtain the corresponding operation results; Use the same method and in combination with the obtained operation results to obtain the adjusted carbon neutralization coefficient, and record the obtained carbon neutralization coefficient as the second carbon neutralization coefficient Tz i ; Then compare the obtained second carbon neutralization coefficient with the neutralization threshold; If Tz i ≤ tz, then generate the corresponding secondary warning and send the corresponding emission reduction path to the monitoring center so that the management personnel of the monitoring center can quickly adjust the emission reduction and carbon absorption strategies for the corresponding monitoring sub-region i; If Tz iIf tz is exceeded, a first-level warning is generated, and the obtained data change curve is fed back to the monitoring center so that the management personnel of the monitoring center can quickly formulate new emission reduction and carbon absorption strategies at the center.

[0009] Further, the process of visualizing the warning information by the data visualization module according to the carbon monitoring analysis model includes: When the monitoring center receives the warning information, it sends the warning information into the carbon monitoring analysis model and determines the corresponding warning color information. The monitoring center displays the monitoring sub-regions that do not require adjustment of the emission reduction and carbon absorption strategies in green; the monitoring center displays the monitoring sub-regions that generate second-level warnings in yellow and issues second-level warnings; the monitoring center displays the monitoring sub-regions that generate first-level warnings in red and issues first-level warnings.

[0010] Compared with the prior art, the beneficial effects of the present invention are as follows. Through the Internet of Things technology, the real-time collection, transmission, and processing of monitoring data are realized, the monitoring efficiency is improved, problems can be discovered and solved in a timely manner, and it is beneficial to provide a scientific basis for formulating emission reduction and carbon absorption strategies; by non-equally dividing the monitoring area to obtain several monitoring sub-regions with unequal areas, the system can achieve refined monitoring and more accurately reflect the actual situation of the monitoring area. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a schematic diagram of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0012] As Figure 1 shown, an emission reduction and carbon absorption monitoring system based on the Internet of Things includes a monitoring center, and the monitoring center is communicatively connected to a data collection module, a data processing module, a data analysis module, a data control module, and a data visualization module; The data collection module is used to divide the monitoring area to obtain several monitoring sub-regions and collect data from the monitoring sub-regions to obtain corresponding monitoring data; The data processing module is used to process the collected monitoring data to obtain a corresponding carbon monitoring analysis model; The data analysis module is used to analyze the collected greenhouse data based on the obtained carbon monitoring analysis model to obtain corresponding warning notifications; The data control module is used to plan corresponding emission reduction paths based on the obtained warning information and verify the corresponding emission reduction paths to obtain corresponding warning information; The data visualization module is used to visually display the warning information according to the carbon monitoring analysis model; It should be further noted that in the specific implementation process, the data acquisition module divides the monitoring area to obtain several monitoring sub-areas, and collects data from the monitoring sub-areas to obtain corresponding monitoring data, including: Obtain a plan view of the monitoring area, and non-equally divide the corresponding monitoring area according to the obtained plan view to obtain several monitoring sub-areas with unequal areas. For example, in an urban area, it can be divided by street routes or administrative regions; Number the obtained monitoring sub-areas, denoted as i, where i = 1, 2, ……, n1, n1 > 0 and n1 is an integer; Set acquisition nodes, and associate the set acquisition nodes with the monitoring sub-areas, where one acquisition node corresponds to one monitoring sub- area; Collect data from the corresponding monitoring sub-areas through the acquisition nodes to obtain corresponding greenhouse data and operation parameters of carbon absorption equipment. The greenhouse data includes carbon emissions, temperature, humidity, wind speed, and greenhouse gas concentration; At the same time, collect data from the corresponding monitoring sub-areas through GIS technology to obtain corresponding geographical data. The geographical data includes vegetation coverage area, greening area, and carbon absorption equipment distribution data; Package the collected greenhouse data, operation parameters of carbon absorption equipment, and geographical data to obtain corresponding monitoring data, and upload the obtained monitoring data to the monitoring center for storage; It should be further noted that in the specific implementation process, the process of the data processing module processing the collected monitoring data to obtain a corresponding carbon monitoring analysis model includes: Read the collected greenhouse data, construct a two-dimensional rectangular coordinate system of time with respect to greenhouse data, and generate corresponding greenhouse gas change curves according to the change amount of greenhouse data. The greenhouse gas change curves include greenhouse gas concentration change curves, carbon emission data change curves, etc.; Obtain the operation parameters of carbon absorption equipment from the monitoring center, input the obtained operation parameters, monitoring data, and geographical data into computer software, and establish a mathematical model based on the physical structure; According to the obtained greenhouse data change curves, obtain several data sets of greenhouse data, divide the obtained data sets of greenhouse data to obtain corresponding training sets and test sets, construct an initial prediction model based on a neural network, and perform iterative training on the initial prediction model through the training set until the loss function training is stable, save the model parameters, and then verify the similarity of the output data matrix of the initial prediction model after iterative training through the training set. If the verification is passed, output the corresponding prediction model; the prediction model is used to predict the carbon emissions and greenhouse gas concentration in the corresponding monitoring sub-area i; Read the geographical data within the corresponding monitoring sub-region i from the monitoring center, input the read geographical data into 3D modeling software to obtain the corresponding digital twin model; Match the parameters of the obtained digital twin model, mathematical model, and prediction model to obtain the corresponding carbon monitoring analysis model.

[0013] It should be further noted that in the specific implementation process, the process by which the data analysis module analyzes the collected greenhouse data based on the obtained carbon monitoring analysis model to obtain the corresponding early warning notice includes: Based on the obtained monitoring data, obtain the real-time temperature, real-time humidity, real-time air pressure, and real-time wind speed within the current monitoring sub-region i, and mark them as WD i 、SD i 、QY i and FS i ; Dynamically evaluate the auxiliary ability provided by the carbon absorption effect within the corresponding monitoring sub-region i based on the obtained real-time temperature, real-time humidity, real-time air pressure, and real-time wind speed to obtain the corresponding auxiliary ability coefficient, and denote the obtained auxiliary ability coefficient as FZ i ; Among them, ; In the formula, λ1, λ2, λ3, and λ4 are weight values, which are specifically determined according to actual needs, but λ4 > λ1 > λ3 > λ2; Set the auxiliary coefficient threshold FZ0; If FZ i ≤FZ0, then the corresponding auxiliary factor is determined as the first auxiliary factor FY i0 ; If FZ i >FZ0, then the corresponding auxiliary factor is determined as the second auxiliary factor FY i1 ; Determine the number of carbon absorption devices within the corresponding monitoring sub-region i according to the distribution of carbon absorption devices in the geographical data, and number them, denoted as j, where j = 1, 2,..., n2, n2 > 0 and n2 is an integer; Obtain the absorption amount and working duration of the corresponding carbon absorption device i, and obtain the average absorption efficiency of the corresponding carbon absorption device i based on them, denoted as TX j , and further based on the obtained average absorption efficiency TX j of the carbon absorption device i, obtain the average carbon absorption capacity within the corresponding monitoring sub-region i, and denote the obtained average carbon absorption capacity as PJ i ; Among them, ; Mark the vegetation coverage area and greening area in the geographical data corresponding to the monitoring sub-region i, and denote them as LM i and LH i respectively; and according to the carbon absorption factor obtained in the corresponding monitoring sub-region i, denote the obtained carbon absorption factor as TY i ; Among them, ; In the formula, is the weight value, which is determined according to actual needs; Set the sampling interval, which consists of the t1 moment and the t2 moment, where the t1 moment represents the current moment and the t2 represents the historical moment; According to the obtained greenhouse data change curve, sample the greenhouse data within the sampling interval to obtain several sampling points, and summarize the greenhouse data corresponding to the sampling points to obtain the corresponding historical greenhouse data packet; Input the obtained greenhouse data packet into the carbon monitoring and analysis model to obtain the data change curve corresponding to the greenhouse data within the prediction interval; the prediction interval consists of the t1 moment and the t3 moment, t3 is the future moment, and the time interval between the t1 moment and the t2 moment is the same as the time interval between the t1 moment and the t3 moment; According to the obtained data change curve, obtain the change curve of the carbon emission data within the corresponding prediction interval, Use the same method to sample the change curve of the carbon emission data within the prediction interval to obtain several sampling points, and summarize the carbon emission data corresponding to the sampling points to obtain the corresponding carbon emission data packet; Number the carbon emission data in the carbon emission data packet, denoted as k, where k = 1, 2,..., n3, n3 > 0 and n3 is an integer; then the corresponding carbon emission data is expressed as CO ik ; and calculate the corresponding carbon emission factor according to it, and denote the obtained carbon emission factor as TP i ; Among them, ; Furthermore, according to the obtained carbon emission factor and carbon absorption factor, obtain the corresponding first carbon neutralization coefficient of the monitoring sub-region i, and denote the obtained first carbon neutralization coefficient as TZ i ; Among them, ; Set the neutralization threshold tz; compare the obtained first carbon neutralization coefficient TZ i with the neutralization threshold tz; If TZ i ≤ tz, it indicates that the emission reduction and carbon absorption strategies in the corresponding monitoring sub-region i do not need to be adjusted, and no other operations are performed; If TZ i > tz, it indicates that the emission reduction and carbon absorption strategies in the corresponding monitoring sub-region i do not meet the requirements, then a warning notice is generated and sent to the data control module.

[0014] It should be further noted that in the specific implementation process, the process by which the data control module plans the corresponding emission reduction path based on the obtained warning notice and verifies the corresponding emission reduction path to obtain the corresponding warning information includes: The warning information includes a first-level warning and a second-level warning; Plan the carbon emission path based on the obtained greenhouse data and carbon monitoring analysis model to obtain the corresponding first emission reduction path, and the first emission reduction path appropriately adjusts the proportion of energy consumption in the corresponding monitoring region i; for example, increase the proportion of renewable energy consumption in the corresponding monitoring sub-region i; Furthermore, re-plan in combination with the obtained first emission reduction path to obtain the corresponding second emission reduction path, and the second emission reduction path refers to optimizing or adding carbon absorption equipment in the corresponding monitoring sub-region i on the premise of energy conservation and emission reduction in the first emission reduction path; Adjust the parameters of the corresponding carbon monitoring analysis model according to the obtained emission reduction path and perform a simulation run to obtain the corresponding operation result; Use the same method and combine the obtained operation result to obtain the adjusted carbon neutralization coefficient, and record the obtained carbon neutralization coefficient as the second carbon neutralization coefficient Tz i ; Then compare the obtained second carbon neutralization coefficient with the neutralization threshold; If Tz i ≤ tz, then generate the corresponding second-level warning and send the corresponding emission reduction path to the monitoring center so that the management personnel of the monitoring center can quickly adjust the emission reduction and carbon absorption strategies for the corresponding monitoring sub-region i; If Tz i > tz, then generate a first-level warning and feedback the obtained data change curve to the monitoring center so that the management personnel of the monitoring center can quickly formulate new emission reduction and carbon absorption strategies for the center.

[0015] It should be further noted that in the specific implementation process, the process by which the data visualization module visually displays the warning information according to the carbon monitoring analysis model includes: After the monitoring center receives the warning information, it sends the warning information into the carbon monitoring and analysis model and determines the corresponding warning color information. The monitoring center displays the monitoring sub-regions that do not require adjustment of emission reduction and carbon absorption strategies in green; the monitoring center displays the monitoring sub-regions that generate secondary warnings in yellow and issues secondary warnings; the monitoring center displays the monitoring sub-regions that generate primary warnings in red and issues primary warnings.

[0016] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An emission reduction and carbon absorption monitoring system based on the Internet of Things, characterized in that, It includes a monitoring center, which is communicatively connected to a data acquisition module, a data processing module, a data analysis module, a data control module, and a data visualization module; The data acquisition module is used to divide the monitoring area to obtain a number of monitoring sub-areas, and collect data from the monitoring sub-areas to obtain corresponding monitoring data; The data processing module is used to process the collected monitoring data to obtain a corresponding carbon monitoring analysis model; The data analysis module is used to analyze the collected monitoring data based on the obtained carbon monitoring analysis model to obtain corresponding warning notifications; The data control module is used to plan corresponding emission reduction paths based on the obtained warning information and verify the corresponding emission reduction paths to obtain corresponding warning information; The data visualization module is used to visually display the warning information according to the carbon monitoring analysis model.

2. The emission reduction and carbon absorption monitoring system based on the Internet of Things according to claim 1, wherein The data acquisition module divides the monitoring area to obtain a number of monitoring sub-areas, and collects data from the monitoring sub-areas to obtain corresponding monitoring data, including: Obtain a floor plan of the monitoring area, and non-uniformly divide the corresponding monitoring area based on the obtained floor plan to obtain a number of monitoring sub-areas with unequal areas; Set up acquisition nodes, and collect data from the corresponding monitoring sub-areas through the acquisition nodes to obtain corresponding greenhouse data and operation parameters of carbon absorption equipment; Collect data from the corresponding monitoring sub-areas through GIS technology to obtain corresponding geographical data; Package the collected greenhouse data, operation parameters of carbon absorption equipment, and geographical data to obtain corresponding monitoring data, and upload the obtained monitoring data to the monitoring center for storage.

3. The emission reduction and carbon absorption monitoring system based on the Internet of Things according to claim 2, characterized in that, The process by which the data processing module processes the collected monitoring data to obtain a corresponding carbon monitoring analysis model includes: Obtain the operation parameters of the carbon absorption equipment from the monitoring center, input the obtained operation parameters, monitoring data, and geographical data into computer software, and establish a mathematical model based on the physical structure; Construct an initial prediction model based on a neural network and perform iterative training to obtain a corresponding prediction model, and input the collected geographical data into 3D modeling software to obtain a corresponding digital twin model; Match the parameters of the obtained digital twin model, mathematical model, and prediction model to obtain a corresponding carbon monitoring analysis model.

4. An emission reduction and carbon absorption monitoring system based on the Internet of Things according to claim 3, characterized in that, The process by which the data analysis module is used to analyze the collected monitoring data based on the obtained carbon monitoring analysis model includes: Calculate the carbon absorption capacity in the corresponding monitoring sub-area based on the collected greenhouse data to obtain a corresponding carbon absorption factor; Predict the greenhouse data in the corresponding monitoring sub-area based on the collected greenhouse data and the obtained carbon monitoring analysis model to obtain a corresponding prediction result, and obtain a corresponding carbon emission factor according to the prediction result; Obtain a corresponding first carbon neutralization coefficient based on the obtained carbon emission factor and carbon absorption factor, and evaluate the emission reduction and carbon absorption strategies in the corresponding monitoring sub-area based on the obtained first carbon neutralization coefficient.

5. The emission reduction and carbon absorption monitoring system based on the Internet of Things according to claim 4, characterized in that, The process of evaluating emission reduction and carbon absorption strategies within the corresponding monitored sub-region based on the obtained first carbon neutrality coefficient includes: Setting a neutralization threshold, comparing the obtained neutralization threshold with the first carbon neutrality coefficient, judging the emission reduction and carbon absorption strategies within the corresponding monitored sub-region based on the comparison result, determining whether they meet the requirements, and if not, generating a corresponding warning notice.

6. The emission reduction and carbon absorption monitoring system based on the Internet of Things according to claim 5, characterized in that, The process by which the data control module plans the corresponding emission reduction path based on the obtained warning notice includes: Planning the carbon emission path based on the obtained greenhouse data and carbon monitoring analysis model to obtain the corresponding first emission reduction path, and further planning again in combination with the obtained first emission reduction path to obtain the corresponding second emission reduction path.

7. An emission reduction and carbon absorption monitoring system based on the Internet of Things according to claim 1, characterized in that, The process of verifying the corresponding emission reduction path and obtaining the corresponding warning information includes: Adjusting the parameters of the corresponding carbon monitoring analysis model according to the obtained emission reduction path and performing a simulation run to obtain the corresponding operation result; Using the same method and combining the obtained operation result to obtain the adjusted second carbon neutrality coefficient; Evaluating the corresponding emission reduction and carbon absorption strategies based on the obtained second carbon neutrality coefficient. If it is necessary to adjust the emission reduction and carbon absorption strategies within the corresponding monitored sub-region, a secondary warning is generated; if it is necessary to re-formulate the emission reduction and carbon absorption strategies within the corresponding monitored sub-region, a primary warning is generated.

8. An emission reduction and carbon absorption monitoring system based on the Internet of Things according to claim 7, characterized in that, The process by which the data visualization module visually displays the warning information according to the carbon monitoring analysis model includes: When the monitoring center receives the warning information, it sends the warning information into the carbon monitoring analysis model and determines the corresponding warning color information. The monitoring center displays the monitored sub-regions that do not require adjustment of the emission reduction and carbon absorption strategies in green; the monitoring center displays the monitored sub-regions that generate secondary warnings in yellow and issues a secondary warning; the monitoring center displays the monitored sub-regions that generate primary warnings in red and issues a primary warning.

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