Regional carbon emission monitoring operation and maintenance management system based on multi-dimensional data

Abstract

The invention relates to the technical field of carbon emission data management, in particular to a regional carbon emission monitoring operation and maintenance management system based on multi-dimensional data, which comprises the following steps: acquiring a geographic position similar to a target region according to geographic features of the target region, analyzing monthly activity data in the similar geographic position, determining a standard activity factor, and determining a regional carbon emission monitoring operation and maintenance management result. The standard activity factors are combined with the annual activity data of the target area, then the annual activity data of the target area are converted into monthly data, then the activity data and electric energy are calculated through an autoregressive distributed lag model, a correlation model is determined, and then the carbon emission value of the target area is estimated by monitoring the electric energy. The carbon emission in the agricultural production process can be monitored in real time, so that the change of the carbon emission in the agricultural production process is reflected in real time, and support is provided for the environmental protection level and sustainability of agricultural production.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission data management, and in particular to a regional carbon emission monitoring and operation management system based on multidimensional data. Background Art

[0002] With the development of the rural economy, rural energy consumption is gradually shifting from traditional biomass energy (such as straw and firewood) to electricity, and there is a certain correlation between changes in rural electricity activities and changes in carbon emissions.

[0003] The prior art CN117216342A discloses a blockchain-based carbon emission visualization tracking and display method and system, including periodically collecting the carbon emissions of all carbon emission enterprises in the target monitoring area to obtain the corporate carbon emissions set of all the carbon emission enterprises, and obtaining the proportion of each carbon emission enterprise in the total regional carbon emissions of the target monitoring area based on the corporate carbon emissions set; obtaining the corporate carbon emission control amount set of all the carbon emission enterprises, and each of the carbon emission enterprises adjusts the carbon emissions in real time according to the corresponding corporate carbon emission control amount and records the change status of the corporate carbon emissions in real time; and visually displaying the real-time change status of the corporate carbon emissions of each carbon emission enterprise and the regional carbon emissions total in real time.

[0004] However, when monitoring rural carbon emissions, due to the different geographical locations of each region, the geographical advantages used in the process of agricultural production activities are also different. The data on agricultural resources consumed in each region, including fertilizers, pesticides, agricultural films, etc., will be different. In addition, due to the high degree of dispersion of agricultural production activities, the consumption of agricultural resources cannot be counted in real time. Therefore, the correlation between agricultural production activities and changes in electricity activities cannot be directly obtained. If rural carbon emissions are obtained only by estimating changes in electricity activities based on manual experience, the accuracy of carbon emission monitoring will be reduced. Summary of the invention

[0005] The purpose of the present invention is to solve the problems in the background technology and to propose a regional carbon emission monitoring, operation and maintenance management system based on multidimensional data.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] Regional carbon emission monitoring and operation management system based on multi-dimensional data, including:

[0008] An information collection module is used to collect regional information of a target area and transmit it to a data storage module. The target area refers to an agricultural industry demonstration park.

[0009] A data storage module is used to store regional information and energy data, and a two-way communication connection is established between the data storage module and the inertial analysis module;

[0010] An inertia analysis module, used to split the annual activity data in the regional information of the target area into monthly data;

[0011] By retrieving key matching information on the Internet based on the geographical characteristics of the target area, and setting the cycle time in the key matching information to natural months, the control data is obtained, and then the control data is analyzed to determine the standard activity factor for each natural month, and then the standard activity factor of the target area is combined with the annual activity data to obtain monthly data;

[0012] Obtain energy data of the target area, divide the energy data into basic equipment data and indirect energy consumption data, and then use the autoregressive distributed lag model to calculate the correlation model between the indirect energy consumption data and the monthly data;

[0013] The regional emission analysis module is used to collect the electric energy of the target area in real time, bring the real-time collected electric energy into the correlation model for calculation, and determine the carbon emission estimation value. The regional emission analysis module then transmits the carbon emission estimation value to the operation and maintenance supervision module;

[0014] The operation and maintenance supervision module is used to dynamically display the estimated carbon emissions of the target area.

[0015] As a further solution of the present invention, the inertia analysis module includes a data conversion unit and a correlation model analysis unit. The data conversion unit is used to calculate the monthly data, and the correlation model analysis unit is used to calculate the correlation model. There is a one-way communication connection between the data conversion unit and the correlation model analysis unit.

[0016] As a further solution of the present invention, the method for the data conversion unit to calculate the monthly data includes:

[0017] S1: Extract activity data of the target area in natural years, where the activity data include the consumption of fertilizers, pesticides, agricultural films and diesel, as well as land tillage and land irrigation values;

[0018] S2: Obtain the geographical features of the target area, use the geographical features of the target area as the search background information, use the activity data as the search keywords, conduct information search on the Internet, and obtain key matching information, where the geographical features include topography, climate conditions, water resource distribution, soil type and its fertility;

[0019] Set the cycle time of the activity data to a natural month, filter the key matching information, and mark the filtered information as control data;

[0020] S3: Taking months as units, divide the natural year into i months, i = 1, 2, ..., 12, then correspond the natural months in the reference data with the months in the natural year, and calculate the unit factor of the natural month;

[0021] S4: Calculate the unit factor Xi of each natural month in the natural year according to the method in step S3;

[0022] Then use the formula Get the standard activity factor Yi of the natural month;

[0023] Furthermore, the standard activity factor is the monthly proportion coefficient of activity data in a natural year, and

[0024]

[0025] S5: Then, the activity data of the target area are multiplied by the standard activity factor Yi to obtain the monthly data HDi of the target area.

[0026] As a further embodiment of the present invention, the method for calculating the unit factor includes:

[0027] Randomly select a natural month as the target analysis month, extract the control data in the target analysis month, and re-label it as the target data;

[0028] Extract the agricultural planting area corresponding to the location area of ​​the target data, divide the target data by the agricultural planting area, and mark the result as unit data;

[0029] At the same time, for the natural year where the target data is located, the annual activity data of the location area corresponding to the target data in this natural year is obtained, and the annual activity data is divided by the agricultural planting area of ​​the location area, and the result is marked as regional conversion data;

[0030] Then, in the target analysis month, the unit data is divided by the regional conversion data to obtain the conversion factor, and then the conversion factors in the target analysis month are averaged and the average calculation result is marked as the unit factor.

[0031] As a further solution of the present invention, a method for calculating a correlation model between indirect energy consumption data and monthly data includes:

[0032] SS1: Extract historical energy data in the target area and divide the energy data into natural months to obtain monthly electricity data;

[0033] According to the energy use area, the monthly power data is divided into basic equipment data and indirect energy consumption data. The basic equipment data refers to the power consumed by the basic equipment in the target area, including lighting equipment, and the indirect energy consumption data refers to the remaining monthly power data in the target area except the power consumed by the basic equipment;

[0034] SS2: Mark the monthly data and indirect energy consumption data of each natural month as data groups, obtain multiple data groups and use them as experimental groups, use the autoregressive distributed lag model to divide the experimental groups into time series, first determine the variable lag order, then build the ARDL model, and use the least squares method to estimate the model parameters, and then test the model residuals to determine the association model M1 between monthly data and indirect energy consumption data: Yt = f(Xt), where Yt is the indirect energy consumption data and Xt is the monthly data.

[0035] As a further embodiment of the present invention, a method for determining an estimated carbon emission value includes:

[0036] Obtain the real-time collected electric energy, remove the basic equipment data in the real-time collected electric energy, and mark the remaining data as indirect data;

[0037] Substitute the indirect data into the correlation model M1 as Yt to obtain the monthly data Xt;

[0038] Multiply the monthly data by the corresponding carbon source emission coefficient to obtain the carbon source emission value;

[0039] The carbon source emission values ​​in the obtained activity data are then accumulated, and the accumulated result is marked as the carbon emission estimate.

[0040] As a further solution of the present invention, it also includes an energy monitoring module for real-time monitoring and collection of electric energy in the target area, and transmitting the real-time collected electric energy to the regional emission analysis module.

[0041] Compared with the prior art, the advantages of the present invention are:

[0042] The present invention obtains geographical locations similar to the target area according to the geographical features of the target area, analyzes the monthly activity data in the similar geographical locations, determines the standard activity factor, and then combines the standard activity factor with the annual activity data of the target area, thereby converting the annual activity data of the target area into monthly data, thereby increasing the time resolution of the data, improving the accuracy of data analysis, and further enhancing the reliability of trend analysis;

[0043] The present invention uses an autoregressive distributed lag model to calculate activity data and electric energy, determine the correlation model, and then estimate the carbon emission value of the target area by monitoring the electric energy. It can monitor the carbon emissions in the agricultural production process in real time, thereby reflecting the changes in carbon emissions in the agricultural production process in real time, and further provide support for the environmental protection level and sustainability of agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0046] Reference Figure 1 , a regional carbon emission monitoring and operation and maintenance management system based on multi-dimensional data, including information collection module, energy monitoring module, data storage module, inertia analysis module, regional emission analysis module and operation and maintenance supervision module;

[0047] The information collection module is used to collect regional information of the target area, wherein the target area in this embodiment is set as an agricultural industry demonstration park, and the regional information refers to the annual activity data of the target area, and then the information collection module transmits the collected regional information to the data storage module;

[0048] The data storage module is used to store the regional information and energy data of the target area, and the data storage module and the inertial analysis module are bidirectionally connected;

[0049] The inertia analysis module is used to perform inertia analysis on the historical regional information and energy data of the target area, wherein the inertia analysis module includes a data conversion unit and a correlation model analysis unit;

[0050] The data conversion unit is used to convert the annual data collected in the target area into monthly data. The specific conversion methods include:

[0051] S1: Extract activity data of the target area in natural years, where the activity data include the consumption of fertilizers, pesticides, agricultural films and diesel, as well as land tillage and land irrigation values;

[0052] S2: Obtain the geographical features of the target area, use the geographical features of the target area as the search background information, use the activity data as the search keywords, conduct information search on the Internet, and obtain key matching information, where the geographical features include topography, climate conditions, water resource distribution, soil type and fertility, etc.;

[0053] Set the cycle time of the activity data to a natural month, filter the key matching information, and mark the filtered information as control data;

[0054] S3: Taking months as units, divide the natural year into i months, i=1, 2, ..., 12, and then correspond the natural months in the reference data to the months in the natural year;

[0055] Randomly select a natural month as the target analysis month, extract the control data in the target analysis month, and re-label it as the target data;

[0056] Extract the agricultural planting area corresponding to the location area of ​​the target data, divide the target data by the agricultural planting area, and mark the result as unit data;

[0057] At the same time, for the natural year where the target data is located, the annual activity data of the location area corresponding to the target data in this natural year is obtained, and the annual activity data is divided by the agricultural planting area of ​​the location area, and the result is marked as regional conversion data;

[0058] Then, in the target analysis month, the unit data is divided by the regional conversion data to obtain the conversion factor, and then the conversion factors in the target analysis month are averaged and the average calculation result is marked as the unit factor;

[0059] It should be further explained that there will be target data from multiple different natural years or location areas in the target analysis month, so multiple conversion factors will be obtained, and then the average of the multiple conversion factors will be calculated and used as the unit factor;

[0060] S4: Calculate the unit factor Xi of each natural month in the natural year according to the method in step S3;

[0061] Then use the formula Get the standard activity factor Yi of the natural month;

[0062] Furthermore, the standard activity factor is the monthly proportion coefficient of activity data in a natural year, and

[0063]

[0064] S5: Then, the activity data of the target area are multiplied by the standard activity factor Yi to obtain the monthly data HDi of the target area;

[0065] By calculating the standard activity factor of the natural month, the annual activity data of the target area is converted into monthly activity data, thereby improving the accuracy of data analysis and further enhancing the reliability of trend analysis;

[0066] Afterwards, the data conversion unit transmits the monthly data HDi of the target area to the correlation model analysis unit;

[0067] The correlation model analysis unit is used to determine the correlation model between the activity data and the energy data according to the monthly data HDi of the target area. The specific method for determining the correlation model includes:

[0068] SS1: Extract historical energy data in the target area and divide the energy data into natural months to obtain monthly electricity data;

[0069] According to the energy use area, the monthly power data is divided into basic equipment data and indirect energy consumption data. The basic equipment data refers to the power consumed by the basic equipment in the target area, including lighting equipment, and the indirect energy consumption data refers to the remaining monthly power data in the target area except the power consumed by the basic equipment;

[0070] SS2: Mark the monthly data and indirect energy consumption data of each natural month as data groups, obtain multiple data groups and use them as test groups, use the autoregressive distributed lag model, divide the test groups into time series, first determine the variable lag order, then build the ARDL model, and use the least squares method to estimate the model parameters, and then test the model residuals to determine the association model M1 between the monthly data and the indirect energy consumption data: Yt = f(Xt), where Yt is the indirect energy consumption data and Xt is the monthly data;

[0071] It should be further explained that the autoregressive distributed lag model algorithm is a prior art and will not be described in detail here;

[0072] Then the correlation model analysis unit transmits the correlation model M1 to the regional emission analysis module;

[0073] The energy monitoring module is used to monitor the electric energy in the target area and transmit the monitored and collected electric energy to the regional emission analysis module;

[0074] The regional emission analysis module is used to predict the carbon emissions of the target area based on the real-time collected electric energy. The specific prediction methods include:

[0075] Obtain the real-time collected electric energy, remove the basic equipment data in the real-time collected electric energy, and mark the remaining data as indirect data;

[0076] Substitute the indirect data into the correlation model M1 as Yt to obtain the monthly data Xt;

[0077] Multiply the monthly data by the corresponding carbon source emission coefficient to obtain the carbon source emission value;

[0078] Then, the carbon source emission values ​​in the obtained activity data are accumulated, and the accumulated result is marked as the carbon emission estimate;

[0079] In this embodiment, the carbon source emission coefficient of fertilizer is 0.89kg / kg, the carbon source emission coefficient of pesticide is 4.93kg / kg, the carbon source emission coefficient of agricultural film is 5.18kg / kg, the carbon source emission coefficient of diesel is 0.59kg / kg, and the carbon source emission coefficient of land tillage is 312.60kg / km 2 The carbon source emission coefficient of land irrigation is 266.48 kg / km 2 ;

[0080] The regional emission analysis module then transmits the carbon emission estimation value to the operation and maintenance supervision module. The operation and maintenance supervision module is used to dynamically display the carbon emission estimation value of the target area, thereby reflecting the dynamic changes of carbon emissions in the agricultural production process in the agricultural industry demonstration park in real time, and then providing support for the environmental protection level and sustainability of agricultural production.

[0081] The present invention collects regional information of the target area and converts the historical annual agricultural activity data in the target area into monthly agricultural activity data (this process is due to the difficulty of real-time collection of agricultural resources every month in the agricultural production process. Usually, the relevant departments will only summarize and display the annual data. If the relationship between carbon emissions and electricity activities in agricultural activities is analyzed in units of years, the estimated data error is large and the data credibility is low);

[0082] Then collect the power consumption of the above monthly activity data in the current month, and remove the direct power consumption from the power consumption to obtain the indirect power consumption (direct power consumption refers to the power consumed by basic lighting);

[0083] Then, the monthly activity data of each month and the corresponding indirect power consumption are taken as a set of data to obtain multiple sets of data, and the multiple sets of data are used as test groups for simulation calculation to obtain the correlation model between electricity and agricultural activity data;

[0084] Then, the power data of the target area is detected in real time, and the detected power data is brought into the correlation model in real time to obtain real-time agricultural activity data. The real-time agricultural activity data is combined with the corresponding carbon emission factors for calculation, so as to monitor the carbon emissions of agricultural activities in the selected target area in real time and improve the timeliness and accuracy of agricultural carbon emission data monitoring.

[0085] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. Regional carbon emission monitoring and operation management system based on multidimensional data, characterized by: include: An information collection module is used to collect regional information of a target area and transmit it to a data storage module. The target area refers to an agricultural industry demonstration park. A data storage module is used to store regional information and energy data, and a two-way communication connection is established between the data storage module and the inertial analysis module; An inertia analysis module, used to split the annual activity data in the regional information of the target area into monthly data; By retrieving key matching information on the Internet based on the geographical characteristics of the target area, and setting the cycle time in the key matching information to natural months, the control data is obtained, and then the control data is analyzed to determine the standard activity factor for each natural month, and then the standard activity factor of the target area is combined with the annual activity data to obtain monthly data; Obtain energy data of the target area, divide the energy data into basic equipment data and indirect energy consumption data, and then use the autoregressive distributed lag model to calculate the correlation model between the indirect energy consumption data and the monthly data; The regional emission analysis module is used to collect the electric energy of the target area in real time, bring the real-time collected electric energy into the correlation model for calculation, and determine the carbon emission estimation value. The regional emission analysis module then transmits the carbon emission estimation value to the operation and maintenance supervision module; The operation and maintenance supervision module is used to dynamically display the estimated carbon emissions of the target area.

2. The regional carbon emission monitoring, operation and maintenance management system based on multidimensional data according to claim 1 is characterized in that: The inertia analysis module includes a data conversion unit and a correlation model analysis unit. The data conversion unit is used to calculate the monthly data, and the correlation model analysis unit is used to calculate the correlation model. There is a one-way communication connection between the data conversion unit and the correlation model analysis unit.

3. The regional carbon emission monitoring, operation and maintenance management system based on multidimensional data according to claim 2 is characterized in that: The data conversion unit calculates monthly data by: S1: Extract activity data of the target area in natural years, where the activity data include the consumption of fertilizers, pesticides, agricultural films and diesel, as well as land tillage and land irrigation values; S2: Obtain the geographical features of the target area, use the geographical features of the target area as the search background information, use the activity data as the search keywords, conduct information search on the Internet, and obtain key matching information, where the geographical features include topography, climate conditions, water resource distribution, soil type and its fertility; Set the cycle time of the activity data to a natural month, filter the key matching information, and mark the filtered information as control data; S3: Taking months as units, divide the natural year into i months, i = 1, 2, ..., 12, then correspond the natural months in the reference data with the months in the natural year, and calculate the unit factor of the natural month; S4: Calculate the unit factor Xi of each natural month in the natural year according to the method in step S3; Then use the formula Get the standard activity factor Yi of the natural month; S5: Then, the activity data of the target area are multiplied by the standard activity factor Yi to obtain the monthly data HDi of the target area.

4. The regional carbon emission monitoring, operation and maintenance management system based on multidimensional data according to claim 3 is characterized in that: The unit factor calculation method includes: Randomly select a natural month as the target analysis month, extract the control data in the target analysis month, and re-label it as the target data; Extract the agricultural planting area corresponding to the location area of ​​the target data, divide the target data by the agricultural planting area, and mark the result as unit data; At the same time, for the natural year where the target data is located, the annual activity data of the location area corresponding to the target data in this natural year is obtained, and the annual activity data is divided by the agricultural planting area of ​​the location area, and the result is marked as regional conversion data; Then, in the target analysis month, the unit data is divided by the regional conversion data to obtain the conversion factor, and then the conversion factors in the target analysis month are averaged and the average calculation result is marked as the unit factor.

5. The regional carbon emission monitoring, operation and maintenance management system based on multidimensional data according to claim 2 is characterized in that: The method for calculating the correlation model between indirect energy consumption data and monthly data includes: SS1: Extract historical energy data in the target area and divide the energy data into natural months to obtain monthly electricity data; According to the energy use area, the monthly power data is divided into basic equipment data and indirect energy consumption data. The basic equipment data refers to the power consumed by the basic equipment in the target area, including lighting equipment, and the indirect energy consumption data refers to the remaining monthly power data in the target area except the power consumed by the basic equipment; SS2: Mark the monthly data and indirect energy consumption data of each natural month as data groups, obtain multiple data groups and use them as experimental groups, use the autoregressive distributed lag model to divide the experimental groups into time series, first determine the variable lag order, then build the ARDL model, and use the least squares method to estimate the model parameters, and then test the model residuals to determine the association model M1 between monthly data and indirect energy consumption data: Yt = f(Xt), where Yt is the indirect energy consumption data and Xt is the monthly data.

6. The regional carbon emission monitoring, operation and maintenance management system based on multidimensional data according to claim 1 is characterized in that: The carbon emissions estimates were determined using the following methods: Obtain the real-time collected electric energy, remove the basic equipment data in the real-time collected electric energy, and mark the remaining data as indirect data; Substitute the indirect data into the correlation model M1 as Yt to obtain the monthly data Xt; Multiply the monthly data by the corresponding carbon source emission coefficient to obtain the carbon source emission value; The carbon source emission values ​​in the obtained activity data are then accumulated, and the accumulated result is marked as the carbon emission estimate.

7. The regional carbon emission monitoring, operation and maintenance management system based on multidimensional data according to claim 6 is characterized in that: It also includes an energy monitoring module for real-time monitoring and collection of electric energy in the target area, and transmitting the real-time collected electric energy to the regional emission analysis module.

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