A carbon emission monitoring system and a carbon emission monitoring method

Abstract

The present invention discloses a carbon emission monitoring system and a monitoring method, which belong to the technical field of carbon emission monitoring and management, and include a regional division module, a carbon emission prediction module, a difference calculation and pairing module, and a pairing adjustment module. The regional division module collects the regional types of the subordinate regions, and divides the subordinate regions based on the historical carbon emission data of the subordinate regions, including carbon emission stable regions and carbon emission fluctuation regions. The present invention discloses a carbon emission monitoring system and a monitoring method, which can realize regional division, carbon emission prediction, complementary pairing, and emission reduction strategy positive incentive cycle. By dividing the subordinate regions, combining the carbon emission prediction value and the fluctuation value, the subordinate sub-regions are matched with profit and loss complementary pairs, and decision support is provided for the fluctuation region, which is convenient for realizing the hierarchical monitoring and supervision of carbon emissions in the entire region, and enhances the practicality and functionality.

Description

Technical Field

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

[0002] Carbon emissions refer to the amount of carbon dioxide released into the atmosphere from a specific source per unit time. It is usually measured in tons of carbon dioxide equivalent, which is used to unify the units of contribution of different greenhouse gases to global warming potential. In actual practice, it is necessary to monitor and control carbon emissions in a certain area at a macro level to promote energy conservation and emission reduction.

[0003] A blockchain-based carbon emission monitoring system with publication number CN115983517A includes a connection layer, a message queue layer, a microservice subsystem, Nginx load balancing, a front-end display and a blockchain system. The microservice subsystem is used to realize the calculation, monitoring and application of carbon emission data. Although it can realize the monitoring of carbon emissions, in the actual monitoring process, the subordinate areas usually include commercial areas and industrial areas. The commercial areas are not affected by production plans, so the carbon emission values ​​tend to be stable. In contrast, the industrial areas are engaged in production and manufacturing, so the carbon emissions are easily affected by production plans and orders, resulting in large fluctuations in carbon emissions. The existing carbon emission monitoring is difficult to subdivide different situations within the region, and has the problem of low practicality. It is also impossible to complementarily match the relevant sub-areas within the subordinate areas, and cannot provide data support for production plan adjustments, and cannot positively promote energy conservation and emission reduction, and has the problem of low functionality.

[0004] In view of the above, this case proposes a carbon emission monitoring system and a monitoring method to solve the above technical problems. Summary of the invention

[0005] The present invention aims to at least solve the technical problems existing in the prior art. To this end, the present invention proposes a carbon emission monitoring system and a monitoring method, which solve the above technical problems by improving the detection method and the processing method.

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

[0007] A carbon emission monitoring system includes a regional division module, a carbon emission prediction module, a difference calculation and pairing module, and a pairing adjustment module;

[0008] The regional division module collects the regional types of the subordinate regions and divides the subordinate regions into regions with stable carbon emissions and regions with fluctuating carbon emissions based on the historical carbon emissions data of the subordinate regions;

[0009] The carbon emission prediction module calculates the relevant carbon emission prediction value of the stable carbon emission area and the carbon emission fluctuation value of the carbon emission fluctuation area according to the stable carbon emission area and the carbon emission fluctuation area in combination with relevant historical data, and transmits the relevant data to the difference calculation and pairing module;

[0010] The difference calculation and pairing module calculates the carbon emission surplus value based on the carbon emission standard value of the relevant sub-region of the carbon emission stable region and the relevant carbon emission forecast value, calculates the carbon emission loss value based on the carbon emission fluctuation value, and arranges the surplus value and the loss value in descending order, and pairs the relevant regions based on the carbon emission surplus value and the carbon emission loss value;

[0011] The pairing adjustment module obtains emission reduction measures and carbon credit values ​​of relevant subordinate sub-regions based on relevant subordinate sub-regions of the carbon emission fluctuation area, obtains environmental protection values ​​based on relevant emission reduction measures calculations and carbon credit values, arranges them in descending order based on the size of the environmental protection values, sets priorities based on the arrangement order, and performs pairing combinations.

[0012] Furthermore, the regional division module collects the regional types of the subordinate regions, and divides the subordinate regions into carbon emission stable regions and carbon emission fluctuating regions based on the historical carbon emission data of the subordinate regions. The specific steps are as follows:

[0013] For all regions under its jurisdiction, the regional types of the regions under its jurisdiction are collected, and historical data is collected, including the historical carbon emission data of the relevant regions within four years. Based on the historical carbon emission data, the carbon emission change value is calculated;

[0014] Based on the carbon emission change value and the carbon emission change value threshold, the subordinate areas are divided into regions, including carbon emission stable areas and carbon emission fluctuating areas.

[0015] Furthermore, based on the carbon emission change value and the carbon emission change value threshold, the subordinate areas are divided into areas with stable carbon emission and areas with fluctuating carbon emission. The specific steps and algorithm formula are as follows:

[0016] The regional types of the subordinate areas are collected, including industrial areas and commercial areas. The carbon emission values ​​of each sub-area in the subordinate areas within four years and the carbon emission standard values ​​of the relevant sub-areas are collected respectively. The carbon emission change value is calculated based on the carbon emission values ​​of each year. The algorithm formula is:

[0017] Calculate the annual difference in carbon emissions over four years:

[0018] ;

[0019] in, Represents the standard value of carbon emissions in the relevant sub-region, Representative i The difference between the annual carbon emissions and the standard value, i The value range is 1-4. Sort in descending order and get the maximum difference value Minimum difference value , calculate the change value of carbon emissions in the relevant sub-regions , and its algorithm formula is:

[0020] ;

[0021] Based on the regional type of the subordinate areas, set the carbon emission change threshold of industrial areas respectively and the threshold for changes in carbon emissions in commercial areas , and for the average change in carbon emissions in the relevant sub-regions Classification:

[0022] in, represents the average change in carbon emissions in relevant sub-regions of the industrial zone, The average change value of carbon emissions in the relevant sub-regions of the industrial zone is used to divide the subordinate areas into regions, and the carbon emissions change threshold of the industrial zone is used to divide the subordinate areas into regions. and the threshold for changes in carbon emissions in commercial areas The specific steps for making a judgment are:

[0023] when > When , it means that the carbon emissions of the relevant sub-regions of the industrial zone fluctuate greatly, and they are classified as carbon emissions fluctuating areas;

[0024] when ≤ When , it means that the carbon emission fluctuation of the relevant sub-regions of the industrial zone is small, and they are marked separately and transmitted to the difference calculation and pairing module;

[0025] when > When , it means that the carbon emissions of the relevant sub-areas of the commercial area fluctuate greatly, and they are marked separately and transmitted to the difference calculation and pairing module;

[0026] when ≤ When , it means that the carbon emissions in the relevant sub-regions of the commercial area fluctuate little and are classified as stable carbon emissions areas.

[0027] Furthermore, the carbon emission prediction module calculates the relevant carbon emission prediction value of the stable carbon emission area and the carbon emission fluctuation value of the carbon emission fluctuation area according to the stable carbon emission area and the carbon emission fluctuation area in combination with relevant historical data, and transmits the relevant data to the difference calculation and pairing module. The specific steps and algorithm formula are as follows:

[0028] For areas with stable carbon emissions, based on the neural network model, the historical carbon emissions values ​​of relevant sub-areas in each month over four years were collected to establish training sets and validation sets. The training set is the carbon emissions values ​​of each month in the first three years, and the validation set is the carbon emissions values ​​of each month in the fourth year.

[0029] Based on recurrent neural network RNN , set the structure of the neural network model, including the number of neuron layers, the number of neurons in each layer, and the activation function;

[0030] Use the training set to train the neural network model, and use the validation set to evaluate the performance and generalization ability of the trained model. Apply the model to the time period that needs to be predicted, generate the carbon emissions forecast value for each month of this year, accumulate the annual carbon emissions forecast value, and transmit it to the difference calculation and pairing module;

[0031] For areas with fluctuating carbon emissions, the maximum carbon emissions value of the relevant sub-area in the past four years is used as the annual carbon emissions fluctuation value of the relevant sub-area this year and transmitted to the difference calculation and matching module.

[0032] Furthermore, the difference calculation and pairing module calculates the carbon emission surplus value based on the carbon emission standard value of the relevant sub-region of the carbon emission stable region and the relevant carbon emission forecast value, calculates the carbon emission loss value based on the carbon emission fluctuation value, arranges the surplus value and the loss value in descending order, and performs pairing combination of relevant regions in combination with the carbon emission surplus value and the carbon emission loss value, including the following steps:

[0033] Based on the carbon emission prediction module, obtain the annual carbon emission prediction value and annual carbon emission fluctuation value of the relevant sub-region;

[0034] The carbon emission surplus value of the relevant sub-region is calculated by combining the annual carbon emission forecast value with the carbon emission standard value, and the carbon emission loss value of the relevant sub-region is calculated by combining the carbon emission fluctuation value with the carbon emission standard value;

[0035] The carbon emission surplus values ​​and carbon emission deficit values ​​are arranged in descending order, and the relevant sub-regions are paired and combined based on the numerical values ​​of the carbon emission surplus values ​​and the carbon emission deficit values.

[0036] Furthermore, the carbon emission surplus value and the carbon emission deficit value are calculated by combining the annual carbon emission forecast value, the annual carbon emission fluctuation value and the carbon emission standard value of the relevant sub-region, and the relevant sub-regions are paired based on the carbon emission surplus value and the carbon emission deficit value. The specific steps and algorithm formula are as follows:

[0037] Carbon emissions prediction based on relevant sub-regions , Fluctuation value of carbon emissions in relevant sub-regions and the carbon emission standard values ​​of the relevant sub-regions , calculate the surplus value of the relevant sub-area and the loss value of the relevant sub-area, and the algorithm formula is:

[0038] ;

[0039] ;

[0040] in, Represents the surplus value of the sub-region of the carbon emission stabilization area of ​​the relevant business district, Represents the loss value of the sub-region of the area of ​​carbon emission fluctuation in the relevant industrial area;

[0041] Based on surplus value and loss value Sort in descending order, for ≥0 and The data with a value greater than or equal to 0 are recorded in two number sequences A and B respectively. Through dynamic programming, a subset of B is found for the number sequences A and B, so that the sum of the elements of the subset is equal to the sum of one or more numbers in A, and the relevant sub-regions are paired;

[0042] for <0 and Data with a value less than 0 are removed and marked separately.

[0043] Furthermore, the difference calculation and pairing module is also provided with an unconventional data processing system, which processes the unconventional data in the carbon emission prediction module and the difference calculation and pairing module separately, and the specific steps are as follows:

[0044] Receive non-routine data from the difference calculation and pairing module and the carbon emission prediction module, including > When the relevant sub-regions are marked separately, ≤ , <0 and <0, and manually analyze the unconventional areas and perform paired grouping separately.

[0045] Further, the pairing adjustment module obtains emission reduction measures for the relevant subordinate sub-regions based on the relevant subordinate sub-regions of the carbon emission fluctuation region, calculates environmental protection values ​​based on the relevant emission reduction measures, arranges them in descending order based on the size of the environmental protection values, sets priorities based on the arrangement order, and performs pairing combinations. The specific steps are:

[0046] Set up an environmental value conversion formula, collect the tons of carbon dioxide equivalent reduced by the emission reduction measures of the relevant subordinate sub-regions in the previous year, convert the environmental value based on the carbon credit value, and sort the relevant sub-regions in descending order based on the converted environmental value;

[0047] Based on the result of descending sorting, the transmission sorting order enters the difference calculation and pairing module. Based on the sorting order, the priority is set, and the sequence B in the dynamic programming method is sorted. Based on the priority in sequence B, the subsets in sequence A are preferentially paired.

[0048] Furthermore, the environmental protection value conversion formula is set to collect the tons of carbon dioxide equivalent reduced by the emission reduction measures of the relevant subordinate sub-regions in the previous year, and convert the environmental protection value in combination with the carbon credit value. Based on the converted environmental protection value, the relevant sub-regions are arranged in descending order. The algorithm formula is:

[0049] ;

[0050] in, Represents the environmental protection value. The larger the environmental protection value, the better the energy conservation and emission reduction measures in the relevant sub-region. Tons of carbon dioxide equivalent reduced by emission reduction measures in the previous year, is the carbon credit value, is the influence coefficient, combined with the environmental protection value The size of the subordinate sub-areas is sorted in descending order.

[0051] A carbon emissions monitoring method comprises the following steps:

[0052] S1. Collect historical carbon emission data of the subordinate regions, and divide the subordinate regions into carbon emission stable regions and carbon emission fluctuating regions. Both the carbon emission stable regions and the carbon emission fluctuating regions contain relevant sub-regions;

[0053] S2. Collect the historical carbon emission values ​​of each month in the four years in the relevant sub-regions in the carbon emission stable region, build a prediction model based on the neural network model, and obtain the annual carbon emission prediction value of the relevant sub-region this year;

[0054] S3. For the carbon emission fluctuation area, the maximum carbon emission value of the relevant sub-area in the past four years shall be used as the annual carbon emission fluctuation value of the relevant sub-area this year;

[0055] S4. Based on the carbon emission standard value of the relevant sub-region, combined with the annual carbon emission forecast value and the annual carbon emission fluctuation value of this year, the carbon emission surplus value and the carbon emission loss value are obtained respectively;

[0056] S5. Based on the relevant subordinate sub-regions of the carbon emission fluctuation area, collect and obtain the emission reduction measures of the relevant subordinate sub-regions, and calculate the environmental protection value. According to the environmental protection value, sort and prioritize the relevant sub-regions, set the priority based on the arrangement order, and pair the relevant sub-regions in combination with the dynamic programming method.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] 1. In the present invention, by setting up a regional division module, the areas under the jurisdiction of the system application are divided in combination with historical carbon emission data, including stable carbon emission areas and fluctuating carbon emission areas. Distinguishing between stable areas and fluctuating areas can more finely manage and monitor carbon emissions, thereby enhancing the accuracy of the system;

[0059] 2. In the present invention, by setting a carbon emission prediction module in conjunction with a regional division module, different prediction strategies are adopted for different regions based on the division results of the regional division module, thereby improving the accuracy of the prediction value in the area with stable carbon emissions, which is helpful for planning long-term emission reduction strategies and resource allocation. At the same time, the carbon emission fluctuation value in the area with fluctuating carbon emissions is obtained, so that managers can adjust strategies and resources in a timely manner;

[0060] 3. In the present invention, by setting a difference calculation and pairing module, combined with the relevant carbon emission forecast value, the surplus value of the relevant sub-region of the stable region is obtained, and the loss value of the relevant sub-region is obtained in combination with the carbon emission fluctuation value of the fluctuating region. The stable region and the fluctuating region are paired and combined through the surplus value and the loss value, so that the fluctuating region can be combined with the actual carbon emission situation of the stable region, and the production plan can be adjusted dynamically in a targeted manner, and the loss value can be compensated by the surplus value to achieve stable carbon emissions;

[0061] 4. In the present invention, based on the results of the pairing combination, the configuration and allocation of the combined resources are optimized, and the carbon emission surplus is provided by the stable area to make up for the carbon emission loss in the fluctuating area, so as to achieve the carbon emission target as a whole, and facilitate the monitoring of the carbon emission situation in the region;

[0062] 5. In the present invention, by setting a pairing adjustment module to cooperate with the difference calculation and pairing module, the environmental protection value of the relevant sub-region is calculated based on the emission reduction measures and plans of the loss-making sub-region in the previous year, and the priority is set based on the environmental protection value, and the surplus area is matched first, so as to encourage the fluctuating area to take more emission reduction measures, form a positive emission reduction cycle, and promote the relevant enterprises to better integrate into the sustainable development strategy;

[0063] 6. In the present invention, based on the combination and pairing of surplus areas and loss areas, multiple combined sub-areas are set to facilitate hierarchical monitoring of carbon emissions within the area, capture the changes and trends of carbon emissions within different areas, enhance the functionality and practicality of the system, and facilitate managers to take targeted management and adjustment measures;

[0064] The entire carbon emissions monitoring system and monitoring method can realize the positive incentive cycle of regional division, carbon emissions prediction, complementary pairing, and emission reduction strategy. By dividing the subordinate areas, combining the carbon emissions prediction value and the fluctuation value, the profit and loss complementary pairing of the subordinate sub-areas is carried out, and decision support is provided for the fluctuating areas, which facilitates the hierarchical monitoring and supervision of carbon emissions in the entire area. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is a block diagram of a carbon emission monitoring system of the present invention. DETAILED DESCRIPTION

[0066] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than 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.

[0067] like Figure 1 As shown, a carbon emission monitoring system includes a regional division module, a carbon emission prediction module, a difference calculation and pairing module, and a pairing adjustment module;

[0068] The regional division module collects the regional types of the subordinate regions and divides the subordinate regions into stable carbon emission regions and fluctuating carbon emission regions based on the historical carbon emission data of the subordinate regions. The specific steps are as follows:

[0069] For all regions under its jurisdiction, the regional types of the regions under its jurisdiction are collected, and historical data is collected, including the historical carbon emission data of the relevant regions within four years. Based on the historical carbon emission data, the carbon emission change value is calculated;

[0070] Based on the carbon emission change value and the carbon emission change value threshold, the subordinate areas are divided into regions, including carbon emission stable areas and carbon emission fluctuating areas;

[0071] The calculated carbon emission change value is combined with the carbon emission change value threshold to divide the subordinate areas into regions, including carbon emission stable areas and carbon emission fluctuation areas. The specific steps and algorithm formula are as follows:

[0072] The regional types of the subordinate areas are collected, including industrial areas and commercial areas. The carbon emission values ​​of each sub-area in the subordinate areas within four years and the carbon emission standard values ​​of the relevant sub-areas are collected respectively. The carbon emission change value is calculated based on the carbon emission values ​​of each year. The algorithm formula is:

[0073] Calculate the annual difference in carbon emissions over four years:

[0074] ;

[0075] in, Represents the standard value of carbon emissions in the relevant sub-region, Representative i The difference between the annual carbon emissions and the standard value, i The value range is 1-4. Sort in descending order and get the maximum difference value Minimum difference value , calculate the change value of carbon emissions in the relevant sub-regions , and its algorithm formula is:

[0076] ;

[0077] Based on the regional type of the subordinate areas, set the carbon emission change threshold of industrial areas respectively and the threshold for changes in carbon emissions in commercial areas , and for the average change in carbon emissions in the relevant sub-regions Classification:

[0078] in, represents the average change in carbon emissions in relevant sub-regions of the industrial zone, The average change value of carbon emissions in the relevant sub-regions of the industrial zone is used to divide the subordinate areas, and the threshold value of carbon emissions change in the industrial zone is combined and the threshold for changes in carbon emissions in commercial areas The specific steps for making a judgment are:

[0079] when > When , it means that the carbon emissions of the relevant sub-regions of the industrial zone fluctuate greatly, and they are classified as carbon emissions fluctuating areas;

[0080] when ≤ When , it means that the carbon emission fluctuation of the relevant sub-regions of the industrial zone is small, and they are marked separately and transmitted to the difference calculation and pairing module;

[0081] when > When , it means that the carbon emissions of the relevant sub-areas of the commercial area fluctuate greatly, and they are marked separately and transmitted to the difference calculation and pairing module;

[0082] when ≤ When , it means that the carbon emissions in the relevant sub-regions of the commercial area fluctuate little and are classified as stable carbon emissions areas.

[0083] It should be noted that the standard values ​​of carbon emissions in relevant sub-regions can be obtained based on regional carbon emissions policies. Represents the carbon emissions value of the previous year. represents the carbon emissions for the first two years, and so on. If the values ​​are equal, = , based on the specific types and impacts of industrial and commercial areas in the relevant areas, and It can be set flexibly. For specific values, you can consult experts in related fields or set them according to actual conditions.

[0084] The carbon emission prediction module calculates the relevant carbon emission prediction values ​​in the stable carbon emission area and the carbon emission fluctuation value in the carbon emission fluctuation area according to the stable carbon emission area and the carbon emission fluctuation area in combination with relevant historical data, and transmits the relevant data to the difference calculation and pairing module. The specific steps and algorithm formula are as follows:

[0085] For areas with stable carbon emissions, based on the neural network model, the historical carbon emissions values ​​of relevant sub-areas in each month over four years were collected to establish training sets and validation sets. The training set is the carbon emissions values ​​of each month in the first three years, and the validation set is the carbon emissions values ​​of each month in the fourth year.

[0086] Based on recurrent neural network RNN , set the structure of the neural network model, including the number of neuron layers, the number of neurons in each layer, and the activation function;

[0087] Use the training set to train the neural network model, and use the validation set to evaluate the performance and generalization ability of the trained model. Apply the model to the time period that needs to be predicted, generate the carbon emissions forecast value for each month of this year, accumulate the annual carbon emissions forecast value, and transmit it to the difference calculation and pairing module;

[0088] For areas with fluctuating carbon emissions, the maximum carbon emissions value of the relevant sub-area in the past four years is used as the annual carbon emissions fluctuation value of the relevant sub-area this year and transmitted to the difference calculation and matching module.

[0089] It should be noted that when collecting the historical carbon emissions values ​​of the relevant sub-regions for each month within four years, the collected data needs to be preprocessed, including but not limited to missing value processing, outlier detection and correction, data smoothing, etc., to ensure the quality and consistency of the data, which is convenient for subsequent model training and verification. At the same time, when collecting the historical carbon emissions values ​​of the relevant sub-regions for each month within four years, the data includes accurate timestamps and corresponding carbon emissions, which is convenient for the subsequent construction of the neural network model.

[0090] The difference calculation and pairing module calculates the carbon emission surplus value based on the carbon emission standard value of the relevant sub-region of the carbon emission stable region and the relevant carbon emission forecast value, and calculates the carbon emission loss value based on the carbon emission fluctuation value. At the same time, the surplus value and the loss value are arranged in descending order, and the relevant regions are paired based on the carbon emission surplus value and the carbon emission loss value. The specific steps are as follows:

[0091] Based on the carbon emission prediction module, obtain the annual carbon emission prediction value and annual carbon emission fluctuation value of the relevant sub-region;

[0092] The carbon emission surplus value of the relevant sub-region is calculated by combining the annual carbon emission forecast value with the carbon emission standard value, and the carbon emission loss value of the relevant sub-region is calculated by combining the carbon emission fluctuation value with the carbon emission standard value;

[0093] The carbon emission surplus values ​​and carbon emission deficit values ​​are arranged in descending order, and the relevant sub-regions are paired and combined based on the values ​​of the carbon emission surplus values ​​and the carbon emission deficit values;

[0094] Combined with the annual carbon emission forecast value, annual carbon emission fluctuation value and carbon emission standard value of the relevant sub-region, the carbon emission surplus value and carbon emission loss value are calculated, and based on the carbon emission surplus value and carbon emission loss value, the relevant sub-regions are paired and combined. The specific steps and algorithm formula are as follows:

[0095] Carbon emissions prediction based on relevant sub-regions , Fluctuation value of carbon emissions in relevant sub-regions and the carbon emission standard values ​​of the relevant sub-regions , calculate the surplus value of the relevant sub-area and the loss value of the relevant sub-area, and the algorithm formula is:

[0096] ;

[0097] ;

[0098] in, Represents the surplus value of the sub-region of the carbon emission stabilization area of ​​the relevant business district, Represents the loss value of the sub-region of the area of ​​carbon emission fluctuation in the relevant industrial area;

[0099] Based on surplus value and loss value Sort in descending order, for ≥0 and The data with a value greater than or equal to 0 are recorded in two number sequences A and B respectively. Through dynamic programming, a subset of B is found for the number sequences A and B, so that the sum of the elements of the subset is equal to the sum of one or more numbers in A, and the relevant sub-regions are paired;

[0100] for <0 and Data with a value less than 0 are removed and marked separately.

[0101] It should be noted that the dynamic programming method can efficiently solve the subset sum problem by constructing a Boolean array to record all possible subset sums. Based on the dynamic programming method, the loss value and the surplus value are matched to form a combination, which is convenient for users to monitor the carbon emissions within the combination. The surplus value generated in actual situations can facilitate the adjustment of production plans in areas where carbon emissions fluctuate in industrial zones to avoid exceeding carbon emissions standards.

[0102] In the difference calculation and pairing module, an unconventional data processing system is also provided to process the unconventional data in the carbon emission prediction module and the difference calculation and pairing module separately. The specific steps are as follows:

[0103] Receive non-routine data from the difference calculation and pairing module and the carbon emission prediction module, including > When the relevant sub-regions are marked separately, ≤ , <0 and <0, and manually analyze the unconventional areas and perform paired grouping separately.

[0104] It should be noted that since commercial areas do not engage in production work, their carbon emissions are mostly fixed values ​​with small fluctuations, usually without large fluctuations and relatively stable. The standard values ​​of carbon emissions allocated based on relevant policies will usually have a surplus. However, since industrial areas engage in production activities, they are easily affected by order conditions, prone to large fluctuations and carbon emissions deficits. Generally, unconventional data will not appear in the system. When unconventional data appears, the sub-areas associated with the unconventional data will be marked and recorded separately. Combined with the specific conditions of the relevant areas, it will be convenient for experts in related fields to conduct analysis and pair and group carbon emissions separately.

[0105] The pairing adjustment module obtains emission reduction measures for the relevant subordinate sub-regions based on the relevant subordinate sub-regions of the carbon emission fluctuation area, calculates the environmental protection value based on the relevant emission reduction measures, arranges them in descending order based on the size of the environmental protection value, sets the priority based on the arrangement order, and performs pairing combination. The specific steps are as follows:

[0106] Set up an environmental value conversion formula, collect the tons of carbon dioxide equivalent reduced by the emission reduction measures of the relevant subordinate sub-regions in the previous year, convert the environmental value based on the carbon credit value, and sort the relevant sub-regions in descending order based on the converted environmental value. The specific algorithm formula is:

[0107] ;

[0108] in, Represents the environmental protection value. The larger the environmental protection value, the better the energy conservation and emission reduction measures in the relevant sub-region. Tons of carbon dioxide equivalent reduced by emission reduction measures in the previous year, is the carbon credit value, is the influence coefficient, combined with the environmental protection value The size of the subordinate sub-areas is sorted in descending order;

[0109] Based on the result of descending sorting, the transmission sorting order enters the difference calculation and pairing module. Based on the sorting order, the priority is set, and the sequence B in the dynamic programming method is sorted. Based on the priority in sequence B, the subsets in sequence A are preferentially paired.

[0110] It should be noted that the tons of carbon dioxide equivalent reduced by emission reduction measures in the previous year for the relevant subordinate sub-regions are collected, and the tons of carbon dioxide equivalent reduced that have been converted into carbon credits are not included. The impact coefficient can be flexibly set according to the specific application environment, or by consulting experts in related fields.

[0111] The present invention provides a carbon emission monitoring method, comprising the following steps:

[0112] S1. Collect historical carbon emission data of the subordinate regions, and divide the subordinate regions into carbon emission stable regions and carbon emission fluctuating regions. Both the carbon emission stable regions and the carbon emission fluctuating regions contain relevant sub-regions;

[0113] S2. Collect the historical carbon emission values ​​of each month in the four years in the relevant sub-regions in the carbon emission stable region, build a prediction model based on the neural network model, and obtain the annual carbon emission prediction value of the relevant sub-region this year;

[0114] S3. For the carbon emission fluctuation area, the maximum carbon emission value of the relevant sub-area in the past four years shall be used as the annual carbon emission fluctuation value of the relevant sub-area this year;

[0115] S4. Based on the carbon emission standard value of the relevant sub-region, combined with the annual carbon emission forecast value and the annual carbon emission fluctuation value of this year, the carbon emission surplus value and the carbon emission loss value are obtained respectively;

[0116] S5. Based on the relevant subordinate sub-regions of the carbon emission fluctuation area, collect and obtain the emission reduction measures of the relevant subordinate sub-regions, and calculate the environmental protection value. According to the environmental protection value, sort and prioritize the relevant sub-regions, set the priority based on the arrangement order, and pair the relevant sub-regions in combination with the dynamic programming method.

[0117] In the embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation; the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the method of this embodiment.

[0118] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A carbon emissions monitoring system, characterized in that: It includes regional division module, carbon emission prediction module, difference calculation and pairing module, and pairing adjustment module; The regional division module collects the regional types of the subordinate regions and divides the subordinate regions into carbon emission stable regions and carbon emission fluctuating regions based on the historical carbon emission data of the subordinate regions. The specific steps are as follows: For all regions under its jurisdiction, the regional types of the regions under its jurisdiction are collected, and historical data is collected, including the historical carbon emission data of the relevant regions within four years. Based on the historical carbon emission data, the carbon emission change value is calculated; Based on the carbon emission change value and the carbon emission change value threshold, the subordinate areas are divided into regions, including carbon emission stable areas and carbon emission fluctuation areas. The specific steps and algorithm formula are as follows: The regional types of the subordinate areas are collected, including industrial areas and commercial areas. The carbon emission values ​​of each sub-area in the subordinate areas within four years and the carbon emission standard values ​​of the relevant sub-areas are collected respectively. The carbon emission change value is calculated based on the carbon emission values ​​of each year. The algorithm formula is: Calculate the annual difference in carbon emissions over four years: ； in, Represents the standard value of carbon emissions in the relevant sub-region, Representative i The difference between the annual carbon emissions and the standard value, i The value range is 1-4. Sort in descending order and get the maximum difference value Minimum difference value , calculate the change value of carbon emissions in the relevant sub-regions , and its algorithm formula is: ； Based on the regional type of the subordinate areas, set the carbon emission change threshold of industrial areas respectively and the threshold for changes in carbon emissions in commercial areas , and for the average change in carbon emissions in the relevant sub-regions Classification: in, represents the average change in carbon emissions in relevant sub-regions of the industrial zone, The average change value of carbon emissions in the relevant sub-regions of the industrial zone is used to divide the subordinate areas, and the carbon emissions change threshold of the industrial zone is combined and the threshold for changes in carbon emissions in commercial areas The specific steps for making a judgment are: when > When , it means that the carbon emissions of the relevant sub-regions of the industrial zone fluctuate greatly, and they are classified as carbon emissions fluctuating areas; when ≤ When , it means that the carbon emission fluctuation of the relevant sub-regions of the industrial zone is small, and they are marked separately and transmitted to the difference calculation and pairing module; when > When , it means that the carbon emissions of the relevant sub-areas of the commercial area fluctuate greatly, and they are marked separately and transmitted to the difference calculation and pairing module; when ≤ When , it means that the carbon emission fluctuation of the relevant sub-region of the commercial area is small, and it is classified as a stable carbon emission area; The carbon emission prediction module calculates the relevant carbon emission prediction value of the stable carbon emission area and the carbon emission fluctuation value of the carbon emission fluctuation area according to the stable carbon emission area and the carbon emission fluctuation area in combination with relevant historical data, and transmits the relevant data to the difference calculation and pairing module; The difference calculation and pairing module calculates the carbon emission surplus value based on the carbon emission standard value of the relevant sub-region of the carbon emission stable region and the relevant carbon emission forecast value, calculates the carbon emission loss value based on the carbon emission fluctuation value, and arranges the surplus value and the loss value in descending order, and pairs the relevant regions based on the carbon emission surplus value and the carbon emission loss value. The specific steps are as follows: Based on the carbon emission prediction module, obtain the annual carbon emission prediction value and annual carbon emission fluctuation value of the relevant sub-region; Combining the annual carbon emission forecast value of the relevant sub-region with the carbon emission standard value, the carbon emission surplus value of the relevant sub-region is calculated, and combining the carbon emission fluctuation value with the carbon emission standard value to calculate the carbon emission loss value of the relevant sub-region. The specific steps and algorithm formula are as follows: Carbon emissions prediction based on relevant sub-regions , Fluctuation value of carbon emissions in relevant sub-regions and the carbon emission standard values ​​of the relevant sub-regions , calculate the surplus value of the relevant sub-area and the loss value of the relevant sub-area, and the algorithm formula is: ； ； in, Represents the surplus value of the sub-region of the carbon emission stabilization area of ​​the relevant business district, Represents the loss value of the sub-region of the area of ​​carbon emission fluctuation in the relevant industrial area; Based on surplus value and loss value Sort in descending order, for ≥0 and The data with a value greater than or equal to 0 are recorded in two number sequences A and B respectively. Through dynamic programming, a subset of B is found for the number sequences A and B, so that the sum of the elements of the subset is equal to the sum of one or more numbers in A, and the relevant sub-regions are paired; for <0 and Data with a value less than 0 are removed and marked separately; The carbon emission surplus values ​​and carbon emission deficit values ​​are arranged in descending order, and the relevant sub-regions are paired and combined based on the values ​​of the carbon emission surplus values ​​and the carbon emission deficit values; The pairing adjustment module obtains emission reduction measures and carbon credit values ​​of relevant subordinate sub-regions based on relevant subordinate sub-regions of the carbon emission fluctuation area, obtains environmental protection values ​​based on relevant emission reduction measures calculations and carbon credit values, arranges them in descending order based on the size of the environmental protection values, sets priorities based on the arrangement order, and performs pairing combinations.

2. A carbon emission monitoring system according to claim 1, characterized in that: The carbon emission prediction module calculates the relevant carbon emission prediction value of the stable carbon emission area and the carbon emission fluctuation value of the carbon emission fluctuation area according to the stable carbon emission area and the carbon emission fluctuation area in combination with relevant historical data, and transmits the relevant data to the difference calculation and pairing module. The specific steps and algorithm formula are as follows: For areas with stable carbon emissions, based on the neural network model, the historical carbon emissions values ​​of relevant sub-areas in each month over four years were collected to establish training sets and validation sets. The training set is the carbon emissions values ​​of each month in the first three years, and the validation set is the carbon emissions values ​​of each month in the fourth year. Based on recurrent neural network RNN , set the structure of the neural network model, including the number of neuron layers, the number of neurons in each layer, and the activation function; Use the training set to train the neural network model, and use the validation set to evaluate the performance and generalization ability of the trained model. Apply the model to the time period that needs to be predicted, generate the carbon emissions forecast value for each month of this year, accumulate the annual carbon emissions forecast value, and transmit it to the difference calculation and pairing module; For areas with fluctuating carbon emissions, the maximum carbon emissions value of the relevant sub-area in the past four years is used as the annual carbon emissions fluctuation value of the relevant sub-area this year and transmitted to the difference calculation and matching module.

3. A carbon emission monitoring system according to claim 1, characterized in that: The difference calculation and pairing module is also provided with an unconventional data processing system, which processes the unconventional data in the carbon emission prediction module and the difference calculation and pairing module separately. The specific steps are as follows: Receive non-routine data from the difference calculation and pairing module and the carbon emission prediction module, including > When the relevant sub-regions are marked separately, ≤ , <0 and <0, and manually analyze the unconventional areas and perform paired grouping separately.

4. A carbon emission monitoring system according to claim 1, characterized in that: The pairing adjustment module obtains emission reduction measures for the relevant subordinate sub-regions based on the relevant subordinate sub-regions of the carbon emission fluctuation region, calculates environmental protection values ​​based on the relevant emission reduction measures, arranges them in descending order based on the size of the environmental protection values, sets priorities based on the arrangement order, and performs pairing combinations. The specific steps are as follows: Set up an environmental value conversion formula, collect the tons of carbon dioxide equivalent reduced by the emission reduction measures of the relevant subordinate sub-regions in the previous year, convert the environmental value based on the carbon credit value, and sort the relevant sub-regions in descending order based on the converted environmental value; Based on the result of descending sorting, the transmission sorting order enters the difference calculation and pairing module. Based on the sorting order, the priority is set, and the sequence B in the dynamic programming method is sorted. Based on the priority in sequence B, the subsets in sequence A are preferentially paired.

5. A carbon emission monitoring system according to claim 4, characterized in that: The environmental protection value conversion formula is set to collect the tons of carbon dioxide equivalent reduced by the emission reduction measures of the relevant subordinate sub-regions in the previous year, and convert the environmental protection value in combination with the carbon credit value. Based on the converted environmental protection value, the relevant sub-regions are sorted in descending order. The specific algorithm formula is: ； in, Represents the environmental protection value. The larger the environmental protection value, the better the energy conservation and emission reduction measures in the relevant sub-region. Tons of carbon dioxide equivalent reduced by emission reduction measures in the previous year, is the carbon credit value, is the influence coefficient, combined with the environmental protection value The size of the subordinate sub-areas is sorted in descending order.

6. A method for monitoring carbon emissions, characterized in that: The method adopts the carbon emission monitoring system as claimed in any one of claims 1 to 5, comprising the following steps: S1. Collect historical carbon emission data of the subordinate regions, and divide the subordinate regions into carbon emission stable regions and carbon emission fluctuating regions. Both the carbon emission stable regions and the carbon emission fluctuating regions contain relevant sub-regions; S2. Collect the historical carbon emission values ​​of each month in the four years in the relevant sub-regions in the carbon emission stable region, build a prediction model based on the neural network model, and obtain the annual carbon emission prediction value of the relevant sub-region this year; S3. For the carbon emission fluctuation area, the maximum carbon emission value of the relevant sub-area in the past four years shall be used as the annual carbon emission fluctuation value of the relevant sub-area this year; S4. Based on the carbon emission standard value of the relevant sub-region, combined with the annual carbon emission forecast value and the annual carbon emission fluctuation value of this year, the carbon emission surplus value and the carbon emission loss value are obtained respectively; S5. Based on the relevant subordinate sub-regions of the carbon emission fluctuation area, collect and obtain the emission reduction measures of the relevant subordinate sub-regions, and calculate the environmental protection value. According to the environmental protection value, sort and prioritize the relevant sub-regions, set the priority based on the arrangement order, and pair the relevant sub-regions in combination with the dynamic programming method.

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