Carbon emission reduction determination method based on block chain

Abstract

The invention discloses a carbon emission reduction determination method based on a block chain, and relates to the technical field of carbon emission determination. The method comprises the following steps: S1, carrying out gridding processing on a monitoring area, and dividing the monitoring area into a plurality of small area units; s2, extracting a characteristic factor of each small area unit, and exporting a measurement model corresponding to each small area unit based on the characteristic factor; and S3, based on the measurement model of each small area unit, monitoring the three-dimensional space carbon emission of each small area unit through measurement equipment, and transmitting data to a block chain server. The monitoring area is divided into a plurality of small area units, each small area unit is monitored by adopting different measurement models, large-scale invalid monitoring is avoided, the energy-saving and environment-friendly properties are better, and the three-dimensional space carbon emission of each small area unit is monitored through the measurement equipment, so that the monitoring accuracy is improved. And the accuracy and the credibility of a carbon emission analysis processing result are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon emission measurement, and in particular relates to a carbon emission reduction measurement method based on blockchain. Background Art

[0002] The blockchain is a chain of blocks. Each block stores certain information, and they are connected into a chain in the order of their generation. This chain is stored in all servers. As long as there is one server in the entire system that can work, the entire blockchain is safe. These servers are called nodes in the blockchain system, and they provide storage space and computing power support for the entire blockchain system. If you want to modify the information in the blockchain, you must obtain the consent of more than half of the nodes and modify the information in all nodes. These nodes are usually in the hands of different entities, so it is extremely difficult to tamper with the information in the blockchain.

[0003] Carbon emission reduction is to reduce the emission of greenhouse gases such as carbon dioxide. With global warming, the emission of greenhouse gases such as carbon dioxide must be reduced to alleviate the climate crisis of mankind. Developing reliable carbon emission monitoring technology and accurately and comprehensively obtaining carbon emission data can provide strong technical support for the formulation of carbon emission reduction measures and the evaluation of their emission reduction effects. Combining blockchain with carbon emission reduction measurement can make use of the characteristics of blockchain data that are difficult to tamper with and decentralized, making the monitoring information more authentic and reliable. Summary of the invention

[0004] The purpose of the present invention is to provide a carbon emission reduction measurement method based on blockchain, which divides the monitoring area into multiple small area units, adopts different measurement models to monitor each small area unit, avoids large-scale invalid monitoring, and has better energy saving and environmental protection. The three-dimensional spatial carbon emissions of each small area unit are monitored by measuring equipment, thereby improving the accuracy and credibility of the carbon emission analysis and processing results.

[0005] To solve the above technical problems, the present invention is achieved through the following technical solutions: The present invention is a method for measuring carbon emission reduction based on blockchain, comprising the following steps: S1: Grid the monitoring area and divide it into multiple small area units; S2: extracting characteristic factors of each small area unit, and deriving the corresponding measurement model of each small area unit based on the characteristic factors; S3: Based on the measurement model of each small area unit, the three-dimensional carbon emissions of each small area unit are monitored by measuring equipment, and the data is transmitted to the blockchain server; S4: The blockchain server aggregates, analyzes, and processes the data of multiple small area units to obtain the carbon emissions of each small area unit and the total carbon emissions data of the monitoring area, and combines the data with the three-dimensional map to obtain the carbon emission dynamic curve and carbon emission dynamic map; S5: Based on the carbon emission dynamic curve and the carbon emission dynamic map, obtain the carbon emission reduction dynamic curve and the carbon emission reduction dynamic map, and transmit the report to the relevant departments in real time.

[0006] Furthermore, the specific steps of the gridding process in S1 are: S11: Measure the area of ​​the monitoring area by using an area measuring instrument; S12: Based on the area and the specific distribution conditions on site, determine the number of small area units and upload the separation data to the blockchain server.

[0007] Furthermore, the characteristic factors in S2 include crowd density, equipment density and house density, and the location, density and measurement frequency of the measuring equipment are determined based on the respective characteristic factors.

[0008] Furthermore, the measuring device in S3 is configured as a carbon emission detection sensor.

[0009] Furthermore, the monitoring methods of the measuring equipment in S3 include aerial dynamic monitoring and ground fixed-point monitoring; aerial dynamic monitoring is based on GPS positioning and navigation components, drones and carbon emission detection sensors to comprehensively monitor the three-dimensional spatial carbon emissions within the small area unit; the carbon emission detection sensors of the ground fixed-point monitoring are fixedly installed at various monitoring points within the small area unit to monitor the ground carbon emissions data.

[0010] Furthermore, the monitoring items of the measuring equipment in S3 include gaseous pollutants, particulate matter and flue gas parameters; gaseous pollutant monitoring is used to measure the concentration and total emissions of CO2, SO2, and NOx, particulate matter monitoring is used to measure the concentration and total emissions of smoke dust, and flue gas parameters are used to measure flue gas flow rate, flue gas temperature, flue gas pressure, flue gas oxygen content, and flue gas humidity.

[0011] Furthermore, the blockchain server and the measuring device in S4 are connected via a communication module.

[0012] Furthermore, the blockchain server in S4 is also connected to the early warning function module, which is used to mark the risk factor of exceeding the carbon emission standard on the carbon emission dynamic map, and uses red, yellow and green colors to mark it. If there is a risk of exceeding the carbon emission standard, the blockchain server controls the early warning function module to issue an early warning.

[0013] Furthermore, the dynamic carbon emission reduction map in S5 also uses red, yellow and green colors to mark the risk factor of exceeding the carbon emission reduction standard. If there is a risk of exceeding the carbon emission reduction standard, the blockchain server controls the early warning function module to issue an early warning.

[0014] The present invention has the following beneficial effects: The present invention divides the monitoring area into a plurality of small area units, and adopts a different measurement model to monitor each small area unit. If the small area unit has many devices and many houses, the monitoring points and the measurement frequency are increased accordingly. On the contrary, if the small area unit has few devices and few houses, the monitoring points and the measurement frequency are reduced accordingly, thereby avoiding large-scale invalid monitoring and achieving better energy conservation and environmental protection.

[0015] The present invention monitors the three-dimensional carbon emissions of each small area unit through measuring equipment, and then summarizes and processes them through a blockchain server. It also adopts a combination of aerial dynamic monitoring and ground fixed-point monitoring to monitor carbon emissions in real time, thereby improving the accuracy and credibility of carbon emission analysis and processing results.

[0016] The present invention also has an early warning function and is intuitive. By marking the risk factor of exceeding the standard on the dynamic map of carbon emissions and carbon emission reduction, and using red, yellow and green colors to mark, the three-dimensional spatial distribution of carbon emission reduction monitoring data can be visualized. If there is a risk of exceeding the carbon emission standard, the blockchain server controls the early warning function module to issue an early warning, and accurately to the specific location, so that the staff can check it in time. Implementation

[0017] The technical solutions in the embodiments of the present invention are described clearly and completely below.

[0018] The present invention is a method for measuring carbon emission reduction based on blockchain, comprising the following steps: S1: Grid the monitoring area and divide it into multiple small area units; The specific steps of gridding processing are: S11: Measure the area of ​​the monitoring area by using an area measuring instrument; S12: Determine the number of small area units based on the area and the specific distribution conditions on site, and upload the partition data to the blockchain server; S2: Extract characteristic factors of each small area unit, and derive the corresponding measurement model of each small area unit based on the characteristic factors. The characteristic factors include crowd density, equipment density and house density. The location, density and measurement frequency of the measuring equipment are determined based on each characteristic factor. S3: Based on the determination model of each small area unit, the three-dimensional carbon emissions of each small area unit are monitored by measuring equipment, and the data is transmitted to the blockchain server, wherein the measuring equipment is set as a carbon emission detection sensor, and the monitoring methods of the measuring equipment include aerial dynamic monitoring and ground fixed-point monitoring; aerial dynamic monitoring is based on GPS positioning navigation components, drones and carbon emission detection sensors to comprehensively monitor the three-dimensional carbon emissions in the small area unit; the carbon emission detection sensors for ground fixed-point monitoring are fixedly installed at each monitoring point in the small area unit to monitor the ground carbon emission data; the monitoring items of the measuring equipment include gaseous pollutants, particulate matter and flue gas parameters; gaseous pollutant monitoring is used to measure the concentration and total emission of CO2, SO2, and NOx, particulate matter monitoring is used to measure the concentration and total emission of smoke, and flue gas parameters are used to measure flue gas flow rate, flue gas temperature, flue gas pressure, flue gas oxygen content, and flue gas humidity; S4: The blockchain server aggregates, analyzes and processes the data of multiple small area units. The blockchain server is connected to the measuring equipment through a communication module to obtain the carbon emissions of each small area unit and the total carbon emissions data of the monitoring area, and combines the data with the three-dimensional map to obtain the carbon emission dynamic curve and the carbon emission dynamic map. The blockchain server is also connected to the early warning function module, which is used to mark the carbon emission exceeding the standard risk coefficient on the carbon emission dynamic map, and uses red, yellow and green colors to mark. If there is a risk of carbon emissions exceeding the standard, the blockchain server controls the early warning function module to issue an early warning, and accurately to the specific location, so that the staff can check it in time; S5: Based on the carbon emission dynamic curve and the carbon emission dynamic map, the carbon emission reduction dynamic curve and the carbon emission reduction dynamic map are obtained, and the report is transmitted to the relevant departments in real time. The carbon emission reduction dynamic map also uses red, yellow and green colors to mark the risk factor of exceeding the carbon emission reduction standard. If there is a risk of exceeding the carbon emission reduction standard, the blockchain server controls the early warning function module to issue an early warning.

[0019] Example 1 Application in a chemical plant S1: Grid the monitored chemical plant and divide it into multiple small area units; The specific steps of gridding processing are: S11: Measure the area of ​​the chemical plant by using an area measuring instrument; S12: Determine the number of small area units based on the area and the specific distribution conditions on site, and upload the partition data to the blockchain server; S2: Extract characteristic factors of each small area unit, such as chemical equipment density, plant density, and population density in each area, and derive the corresponding measurement model of each small area unit based on the characteristic factors to determine the location, density, and measurement frequency of the measurement equipment; S3: Based on the measurement model of each small area unit, the three-dimensional carbon emissions of each small area unit are monitored by measuring equipment, and the data is transmitted to the blockchain server; S4: The blockchain server aggregates, analyzes and processes the data of multiple small area units. The blockchain server is connected to the measuring equipment through a communication module to obtain the carbon emissions of each small area unit and the total carbon emissions data of the chemical plant, and combines the data with the three-dimensional map to obtain the carbon emission dynamic curve and the carbon emission dynamic map. The blockchain server is also connected to the early warning function module, which is used to mark the carbon emission exceeding the standard risk coefficient on the carbon emission dynamic map, and uses red, yellow and green colors to mark. If there is a risk of carbon emissions exceeding the standard, the blockchain server controls the early warning function module to issue an early warning, and accurately to the specific location, so that the staff can check it in time; S5: Based on the carbon emission dynamic curve and the carbon emission dynamic map, the carbon emission reduction dynamic curve and the carbon emission reduction dynamic map are obtained, and the report is transmitted to the relevant departments in real time. The carbon emission reduction dynamic map also uses red, yellow and green colors to mark the risk factor of exceeding the carbon emission reduction standard. If there is a risk of exceeding the carbon emission reduction standard, the blockchain server controls the early warning function module to issue an early warning.

[0020] Example 2 Application in rural areas S1: Grid the monitored rural area and divide it into multiple small regional units; The specific steps of gridding processing are: S11: Measure the area of ​​the rural area using an area measuring instrument; S12: Determine the number of small area units based on the area and the specific distribution conditions on site, and upload the partition data to the blockchain server; S2: Extract characteristic factors of each small area unit, such as housing density, population density and other characteristic factors that affect carbon emissions, and derive the corresponding measurement model of each small area unit based on the characteristic factors to determine the location, density and measurement frequency of the measurement equipment; S3: Based on the measurement model of each small area unit, the three-dimensional carbon emissions of each small area unit are monitored by measuring equipment, and the data is transmitted to the blockchain server; S4: The blockchain server aggregates, analyzes and processes the data of multiple small regional units. The blockchain server is connected to the measuring equipment through a communication module to obtain the carbon emissions of each small regional unit and the total carbon emissions data of the rural area, and combines the data with the three-dimensional map to obtain the carbon emission dynamic curve and the carbon emission dynamic map. The blockchain server is also connected to the early warning function module, which is used to mark the carbon emission exceeding the standard risk coefficient on the carbon emission dynamic map, and uses red, yellow and green colors to mark. If there is a risk of carbon emissions exceeding the standard, the blockchain server controls the early warning function module to issue an early warning, and accurately to the specific location, so that the staff can check it in time; S5: Based on the carbon emission dynamic curve and the carbon emission dynamic map, the carbon emission reduction dynamic curve and the carbon emission reduction dynamic map are obtained, and the report is transmitted to the relevant departments in real time. The carbon emission reduction dynamic map also uses red, yellow and green colors to mark the risk factor of exceeding the carbon emission reduction standard. If there is a risk of exceeding the carbon emission reduction standard, the blockchain server controls the early warning function module to issue an early warning.

[0021] The above are only preferred embodiments of the present invention and do not limit the present invention. Any modification to the technical solutions recorded in the aforementioned embodiments, any equivalent replacement of some of the technical features therein, and any modification, equivalent replacement, and improvement made are all within the protection scope of the present invention.

Claims

1. A method for measuring carbon emission reduction based on blockchain, characterized in that: The following steps are involved: S1: Grid the monitoring area and divide it into multiple small area units; S2: extracting characteristic factors of each small area unit, and deriving the corresponding measurement model of each small area unit based on the characteristic factors; S3: Based on the measurement model of each small area unit, the three-dimensional carbon emissions of each small area unit are monitored by measuring equipment, and the data is transmitted to the blockchain server; S4: The blockchain server aggregates, analyzes, and processes the data of multiple small area units to obtain the carbon emissions of each small area unit and the total carbon emissions data of the monitoring area, and combines the data with the three-dimensional map to obtain the carbon emission dynamic curve and carbon emission dynamic map; S5: Based on the carbon emission dynamic curve and the carbon emission dynamic map, obtain the carbon emission reduction dynamic curve and the carbon emission reduction dynamic map, and transmit the report to the relevant departments in real time.

2. A blockchain-based carbon reduction measurement method according to claim 1, characterized in that: The specific steps of the gridding process in S1 are: S11: Measure the area of ​​the monitoring area by using an area measuring instrument; S12: Based on the area and the specific distribution conditions on site, determine the number of small area units and upload the separation data to the blockchain server.

3. A blockchain-based carbon emission reduction measurement method according to claim 1, characterized in that: The characteristic factors in S2 include crowd density, equipment density and house density, and the location, density and measurement frequency of the measuring equipment are determined based on the various characteristic factors.

4. A blockchain-based carbon emission reduction measurement method according to claim 1, characterized in that: The measuring device in S3 is configured as a carbon emission detection sensor.

5. A blockchain-based carbon emission reduction measurement method according to claim 1, characterized in that: The monitoring methods of the measuring equipment in S3 include aerial dynamic monitoring and ground fixed-point monitoring; Aerial dynamic monitoring is based on GPS positioning and navigation components, drones, and carbon emission detection sensors to comprehensively monitor the three-dimensional carbon emissions within small regional units; The carbon emission detection sensors for ground fixed-point monitoring are fixedly installed at various monitoring points within the small area unit to monitor ground carbon emission data.

6. A blockchain-based carbon reduction measurement method according to claim 1, characterized in that: The monitoring items of the measuring equipment in S3 include gaseous pollutants, particulate matter and flue gas parameters; Gaseous pollutant monitoring is used to measure the concentration and total emissions of CO2, SO2, and NOx; particulate matter monitoring is used to measure the concentration and total emissions of smoke dust; and flue gas parameters are used to measure flue gas flow rate, flue gas temperature, flue gas pressure, flue gas oxygen content, and flue gas humidity.

7. A blockchain-based carbon reduction measurement method according to claim 1, characterized in that: The blockchain server and the measuring device in S4 are connected via a communication module.

8. A blockchain-based carbon emission reduction measurement method according to claim 1, characterized in that: The blockchain server in S4 is also connected to the early warning function module, which is used to mark the risk factor of carbon emissions exceeding the standard on the carbon emissions dynamic map, and use red, yellow and green colors to mark it. If there is a risk of carbon emissions exceeding the standard, the blockchain server controls the early warning function module to issue an early warning.

9. A blockchain-based carbon reduction measurement method according to claim 1, characterized in that: The carbon emission reduction dynamic map in S5 also uses red, yellow and green colors to mark the risk coefficient of exceeding the carbon emission reduction standard. If there is a risk of exceeding the carbon emission reduction standard, the blockchain server controls the early warning function module to issue an early warning.