Carbon analysis method and device for regional energy, terminal equipment and computer readable storage medium

Abstract

The invention discloses a carbon analysis method and device for regional energy, terminal equipment and a computer readable storage medium. The method comprises the following steps: acquiring carbon emission of each analysis object in each user type in each region in a plurality of historical weeks; constructing a carbon emission broken line graph of each analysis object according to the carbon emission; determining the emission change value, the carbon emission difference value and the high-average ratio value of each analysis object through the carbon emission broken line graph; determining an overall emission change index according to the emission change value of each analysis object and a corresponding preset emission change threshold; according to the carbon rejection value and the high-average ratio value of each analysis object, and a corresponding preset carbon rejection value threshold value and a preset high-average ratio value threshold value, determining a carbon emission abnormity evaluation index of each analysis object; according to the carbon emission abnormity evaluation index of each analysis object, determining a regional emission abnormity evaluation index of each region; and taking the overall emission change index and the regional emission anomaly evaluation index of each region as a carbon analysis result of the regional energy.

Description

Technical Field

[0001] The present invention relates to the field of energy carbon analysis, and in particular to a carbon analysis method, device, terminal equipment and computer-readable storage medium for regional energy. Background Art

[0002] Carbon emissions are the main driving force of climate change: Carbon emissions are one of the main causes of global warming. Increased carbon emissions will lead to environmental pollution and ecological damage, threatening biodiversity and sustainable development. In order to implement low-carbon energy transformation and reduce greenhouse gas emissions, carbon analysis and management of regional energy is needed.

[0003] In the current carbon analysis management, it mainly relies on analyzing the changes in carbon emissions in different regions, and formulating emission reduction strategies by tracking and comparing the carbon emission levels and trends in different regions. However, places with different geography, climate, economic structure and development stage have different carbon emission requirements. For example, economically developed regions may need higher emission reduction targets to lead industry and technological innovation. In addition, due to differences in energy structure and resource availability in different regions, some regions may have more potential for renewable energy, such as solar energy, wind energy, hydropower, etc., which can more conveniently implement clean energy transformation and reduce dependence on fossil fuels and carbon emissions, while other regions may rely more on traditional energy such as fossil fuels. Therefore, it is impossible to conduct a comprehensive analysis of regional energy only through changes in carbon emissions in different regions. Summary of the invention

[0004] The embodiments of the present invention provide a method, apparatus, terminal device and computer-readable storage medium for carbon analysis of regional energy, which can provide a more comprehensive understanding of the carbon emission status of different regions.

[0005] An embodiment of the present invention provides a carbon analysis method for regional energy, comprising:

[0006] Obtain the carbon emissions of each analysis object in each user type in each region for several weeks in history;

[0007] Construct a carbon emission line chart for each analysis object based on carbon emissions;

[0008] According to the slope of each line segment in the carbon emission line graph of each analysis object, the abnormal threshold and the abnormal threshold of each line segment above the line, the emission variation value, carbon emission difference value and high-average ratio of each analysis object are determined;

[0009] Determine the overall emission change index for all regions based on the emission change value of each analysis object and the preset emission change threshold of each user type in each region;

[0010] Determine the carbon emission anomaly assessment index for each analysis object based on the carbon emission anomaly value and high-average ratio of each analysis object, as well as the preset carbon emission anomaly threshold and preset high-average ratio threshold of each user type in each region;

[0011] According to the carbon emission anomaly assessment index of each analysis object, determine the regional emission anomaly assessment index of each region;

[0012] The overall emission change index and the regional emission anomaly assessment index of each region are taken as the carbon analysis results of regional energy.

[0013] Furthermore, the carbon analysis method for regional energy also includes:

[0014] The carbon emission line graph of each analysis object, the emission variation value of each analysis object, the carbon emission difference value of each analysis object, the high-to-average ratio of each analysis object, the overall emission variation index, the carbon emission anomaly assessment index of each analysis object, and the regional emission anomaly assessment index of each region are transmitted to the display device so that the display device converts each data into a visual chart; the visual chart includes: a line chart or a bar chart.

[0015] Furthermore, the carbon analysis method for regional energy also includes:

[0016] Determine the carbon emission change level based on the range of the overall emission change index;

[0017] The regional carbon emission excess level of each region is determined based on the range of the regional emission anomaly assessment index of each region.

[0018] Furthermore, the carbon emissions of each analysis object in each user type in each region for several weeks in history are obtained, including:

[0019] Obtain the traditional energy consumption of each analysis object in each user type in each region for several weeks in history;

[0020] For each analysis object of each user type in each region, the carbon emissions of the analysis object in the past several weeks are calculated based on the traditional energy consumption of the analysis object in the past several weeks and the corresponding carbon emission coefficient.

[0021] Furthermore, based on the emission change values ​​of each analysis object and the preset emission change thresholds of each user type in each area, the overall emission change index of all areas is determined, including:

[0022] For each user type in each area, the emission variation values ​​of each analysis object in the user type are averaged to obtain the emission mean value of the corresponding user type;

[0023] Calculate the absolute difference between the emission variation value of each analysis object in the user type and the emission mean value of the corresponding user type;

[0024] From the absolute differences, select the target absolute differences that meet the preset value range;

[0025] The emission change values ​​corresponding to the target absolute difference are averaged to obtain the emission change assessment index for the corresponding user type;

[0026] Based on the emission change assessment index of each user type in each region and the preset emission change threshold of each user type in each region, the overall emission change index of all regions is calculated by the following formula:

[0027]

[0028] Among them, ZDG represents the overall emission change index, P represents the number of regions, U represents the number of user types, and PFBi j represents the emission change assessment index of the jth user type in the i-th region, represents the preset emission change threshold of the jth user type in the i-th region, hi j Represents the preset weight factor of the emission change assessment index of the j-th user type in the ith region.

[0029] Furthermore, according to the carbon emission abnormality value and high-average ratio of each analysis object, as well as the preset carbon emission abnormality threshold and the preset high-average ratio threshold of each user type in each area, the carbon emission abnormality assessment index of each analysis object is determined, including:

[0030] According to the carbon emission anomaly value and high-average ratio of each analysis object, as well as the preset carbon emission anomaly threshold and preset high-average ratio threshold of each user type in each region, the carbon emission anomaly assessment index of the corresponding analysis object is calculated by the following formula:

[0031]

[0032] Among them, TYQi j,k TUEi represents the carbon emission anomaly evaluation index of the kth analysis object in the jth user type in the i-th region, j,k represents the carbon emission value of the kth analysis object in the jth user type in the i-th region, represents the preset carbon emission outlier threshold of the jth user type in the i-th region, cf1 represents the preset weight factor of the carbon emission outlier, GBi j,k represents the high-to-average ratio of the kth analysis object in the jth user type in the i-th region, represents the preset high-to-average ratio threshold of the j-th user type in the ith area, and cf2 represents the preset weight factor of the high-to-average ratio.

[0033] Furthermore, according to the carbon emission anomaly assessment index of each analysis object, the regional emission anomaly assessment index of each region is determined, including:

[0034] For each user type in each region, the carbon emission anomaly assessment index of each analysis object in the user type is compared with the corresponding preset carbon emission anomaly threshold;

[0035] Filter out the carbon emission anomaly assessment index that is higher than the corresponding preset carbon emission anomaly threshold, and use it as the target carbon emission anomaly assessment index;

[0036] The ratio of the number of target carbon emission abnormality assessment indexes to the number of carbon emission abnormality assessment indexes is calculated to obtain the abnormal height ratio of the corresponding user type;

[0037] Calculate the difference between the target carbon emission anomaly assessment index and the corresponding preset carbon emission anomaly threshold to obtain the target carbon emission anomaly difference;

[0038] Calculate the average of the target carbon emission abnormal difference values ​​within the first preset difference range to obtain a difference average value;

[0039] Calculate the average of the target carbon emission abnormal difference in the second preset difference range to obtain a difference-average binary value; wherein the second preset difference range is higher than the first preset difference range;

[0040] Perform weighted calculation on the difference mean one value and the difference mean two value to obtain the abnormal high estimation value of the corresponding user type;

[0041] The emission anomaly value of the corresponding user type is calculated according to the anomaly ratio of the corresponding user type, the anomaly estimation of the corresponding user type, the preset anomaly ratio threshold of the corresponding user type and the preset anomaly estimation threshold of the corresponding user type;

[0042] For each region, the regional emission anomaly assessment index is calculated based on the emission anomaly values ​​of each user type in the region and the preset weight factors corresponding to the emission anomaly values ​​of each user type.

[0043] Based on the above method embodiment, the present invention provides a corresponding device embodiment, including: a carbon emission acquisition module, a carbon emission line graph construction module, a carbon emission data determination module, an overall emission change index determination module, a carbon emission anomaly assessment index determination module, a regional emission anomaly assessment index determination module, and a carbon analysis result output module;

[0044] The carbon emission acquisition module is used to obtain the carbon emission of each analysis object in each user type in each region for several weeks in history;

[0045] A carbon emission line graph construction module is used to construct a carbon emission line graph of each analysis object according to the carbon emission;

[0046] The carbon emission data determination module is used to determine the emission variation value, carbon emission difference value and high-average ratio of each analysis object according to the slope of each line segment in the carbon emission line graph of each analysis object, the abnormal threshold and the abnormal threshold high line of each line segment;

[0047] An overall emission change index determination module is used to determine the overall emission change index of all areas according to the emission change value of each analysis object and the preset emission change threshold of each user type in each area;

[0048] A carbon emission anomaly assessment index determination module is used to determine the carbon emission anomaly assessment index of each analysis object according to the carbon emission anomaly value and high-to-average ratio of each analysis object, as well as a preset carbon emission anomaly threshold and a preset high-to-average ratio threshold for each user type in each region;

[0049] A regional emission anomaly assessment index determination module is used to determine the regional emission anomaly assessment index of each region according to the carbon emission anomaly assessment index of each analysis object;

[0050] The carbon analysis result output module is used to output the overall emission change index and the regional emission anomaly assessment index of each region as the carbon analysis result of regional energy.

[0051] Based on the above-mentioned method embodiment, the present invention provides a corresponding terminal device embodiment, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the steps of the carbon analysis method for regional energy as described in the present invention are implemented.

[0052] Based on the above method embodiment, the present invention provides a corresponding computer-readable storage medium embodiment, including: a stored computer program, which controls the device where the computer-readable storage medium is located to execute the steps of the carbon analysis method for regional energy as described in the present invention when the computer program is running.

[0053] Compared with the prior art, the beneficial effects of the embodiment of this solution are:

[0054] The present invention obtains the carbon emissions of each analysis object in each user type in each region for several weeks in history, and constructs a carbon emissions line graph, so as to intuitively display the data change trend. By analyzing the slope of each line segment in the carbon emissions line graph, the abnormal threshold and the abnormal threshold of each line segment above the line, the emission change value, carbon emission difference value and high-to-average ratio can be determined to evaluate the emission change and abnormal situation of the analysis object. Then, according to the emission change value of each analysis object and the preset emission change threshold of each user type in each region, the overall emission change index of all regions is determined to evaluate the overall emission of the region. According to the carbon emission difference value and high-to-average ratio of each analysis object, and the preset carbon emission difference value threshold and the preset high-to-average ratio threshold of each user type in each region, the carbon emission abnormal evaluation index of each analysis object is determined to evaluate whether the carbon emission of the analysis object is abnormal. In the carbon analysis process, the preset emission change threshold, the preset carbon emission difference value threshold and the preset high-to-average ratio threshold of each user type in each region are introduced, and the carbon emission standards of different user types in different regions are taken into account, thereby further improving the accuracy of carbon analysis management due to differences in regional carbon emission standards. Finally, based on the carbon emission anomaly assessment index of each analysis object, the regional emission anomaly assessment index of each region is determined. Through the regional emission anomaly assessment index, we can have a more comprehensive understanding of the carbon emission status of different regions and provide an important basis for formulating carbon emission reduction strategies and management decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flow chart of a carbon analysis method for regional energy provided by one embodiment of the present invention;

[0056] Figure 2 A carbon emission line graph of an analysis object provided by an embodiment of the present invention;

[0057] Figure 3 is another carbon emission line graph of an analysis object provided by an embodiment of the present invention;

[0058] Figure 4 It is a structural schematic diagram of a carbon analysis device for regional energy provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] In the description of the present invention, it should be understood that the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features.

[0061] like Figure 1 As shown, an embodiment of the present invention provides a carbon analysis method for regional energy, the method comprising at least the following steps:

[0062] Step S1: Obtain the carbon emissions of each analysis object in each user type in each region for several weeks in history;

[0063] In a preferred embodiment, obtaining the carbon emissions of each analysis object in each user type in each region for several weeks in history includes:

[0064] Obtain the traditional energy consumption of each analysis object in each user type in each region for several weeks in history;

[0065] For each analysis object of each user type in each region, the carbon emissions of the analysis object in the past several weeks are calculated based on the traditional energy consumption of the analysis object in the past several weeks and the corresponding carbon emission coefficient.

[0066] For step S1, in this embodiment, the current monitoring and management area is divided according to the region to which it belongs to obtain each area i, where i = 1, 2, ..., P, P is the total number of areas; in each area, each user in the area is divided according to the user type K, and the user type K can take the value of 1, 2 or 3, corresponding to the residential type, commercial type and industrial type respectively.

[0067] X users are randomly selected from different user types in each area as analysis objects, where the screening range for residential users is 20<X<30, the screening range for commercial users is 10<X<15, and the screening range for industrial users is 3<X<6.

[0068] Next, the traditional energy consumption of each analysis object in each user type in each region in the previous seven weeks is obtained, where traditional energy types include: coal, natural gas and oil. The energy consumption corresponding to coal, natural gas and oil is multiplied by the corresponding carbon emission coefficient to obtain the carbon emissions of each traditional energy type. Finally, the carbon emissions of each traditional energy type are added together to obtain the weekly carbon emissions of the corresponding analysis object.

[0069] Step S2: constructing a carbon emission line graph of each analysis object according to the carbon emission;

[0070] For step S2, for each analysis object, according to the weekly carbon emissions of the analysis object obtained in step S1, the weekly carbon emissions are plotted in the form of numerical points, with the horizontal axis representing time and the vertical axis representing carbon emissions. The change line is obtained by connecting adjacent numerical points, and finally a line graph of the carbon emissions of the analysis object is obtained. The line graph can show the changes in the carbon emissions of the analysis object in the past seven weeks. By observing the line graph, it can be seen whether the carbon emissions show an upward, downward or stable trend over time.

[0071] Step S3: Determine the emission variation value, carbon emission difference value and high-average ratio of each analysis object according to the slope of each line segment in the carbon emission line graph of each analysis object, the abnormal threshold and the abnormal threshold of each line segment above the line;

[0072] For step S3, Figure 2 As shown, further analysis is performed on the carbon emission line graph of each analysis object obtained in step S2, and the slope of the change line and the angle between the change line and the horizontal line are calculated to determine the change trend of carbon emissions. First, the slope of each change line is calculated by calculating the difference between two adjacent numerical points, and the difference represents the change in carbon emissions per unit time. By calculating the slope, it can be determined whether the carbon emissions are increasing or decreasing; next, the angle between each change line and the horizontal line is calculated by calculating the difference between the slopes of the change line and the horizontal line. The change trend of emissions can be judged according to the size of the angle. If the angle is an acute angle, it means that the carbon emissions continue to decrease over time, and it is marked as the emission decrease rate. If the angle is an obtuse angle, it means that the carbon emissions continue to increase over time, and it is marked as the emission increase rate. Then, all emission decrease rates are summed to obtain the emission decrease value G1, which represents the total amount of carbon emissions decreased by the analysis object. Similarly, all emission increase rates are summed to obtain the emission increase value G2, which represents the total amount of carbon emissions increased by the analysis object.

[0073] Connect the numerical points corresponding to the first week and the seventh week to obtain a connecting line. The slope of the connecting line represents the average rate of change of carbon emissions in the past seven weeks. Next, calculate the angle between the connecting line and the horizontal line, where the angle can be obtained by calculating the difference between the slope of the connecting line and the slope of the horizontal line. The change trend of emissions can be judged according to the size of the angle. If the angle is an obtuse angle, it means that carbon emissions have continued to rise in the past seven weeks, and the slope of the connecting line is marked as an upward slope G3; if the angle is an acute angle, it means that carbon emissions have continued to decline in the past seven weeks, and the slope of the connecting line is marked as a downward slope G4, and the absolute value is taken.

[0074] Get the positions of the highest and lowest numerical points in the carbon emissions line graph of the analyzed object, and calculate the vertical distance between the highest and lowest numerical points. The vertical distance can be obtained by subtracting the vertical coordinate of the lowest numerical point from the vertical coordinate of the highest numerical point. This vertical distance represents the maximum fluctuation range of carbon emissions in the past seven weeks. This vertical distance is marked as G5.

[0075] The emission variable value LBi of the analysis object is calculated by the following calculation formula j,k :

[0076]

[0077] Among them, LBi j,k represents the emission variation of the kth analysis object in the jth user type in the i-th region, G s Where s = 3 or 4, a1, a2 and a3 are the preset weight factors of each emission variable. Emission variable LBi j,k It reflects the comprehensive changes in emissions of the analyzed object within a specific period of time. This value can be used to evaluate and compare the changing trends of emissions, and provide more comprehensive data support for the formulation of environmental protection rules and energy management rules.

[0078] like Figure 3 As shown, obtain the corresponding weekly carbon emission reference value of the user type in the area to which the analysis object belongs, and draw Figure 3 For data points where carbon emissions are above the threshold line, a vertical line is drawn connecting the data point and the threshold line and marked as High Out Line.

[0079] Calculate the difference between the carbon emissions corresponding to each high-exceeding line of the analysis object and the carbon emissions corresponding to the threshold line to obtain each group of high-exceeding values ​​of the analysis object, which is used to indicate the degree to which each data point of the analysis object exceeds the threshold; then, count the number of high-exceeding values ​​of the analysis object, that is, the number of data points exceeding the threshold, and mark it as the high-exceeding number GCi j,k ; Preset multiple groups of value ranges that match the high-level values, each group of value ranges corresponds to an influence coefficient, that is, each value range represents a different degree of influence, and match each group of high-level values ​​of the analysis object with the preset value range to obtain the influence coefficient corresponding to each group of high-level values; then, multiply each group of high-level values ​​with the corresponding influence coefficient to obtain each group of influence values, which is used to represent the impact emission generated by each high-level value according to its degree of influence; calculate the mean between each group of influence values ​​to obtain the influence mean GTi j,kIn general, by calculating the excess ranking value and the impact ranking value, the degree to which the data points of the analysis object exceed the threshold is quantified, and the different degrees of impact are evaluated according to the preset impact coefficient. The final impact mean value comprehensively reflects the degree of excess and impact of the analysis object.

[0080] Extract the highest weekly carbon emissions of the analyzed object in the past seven weeks and mark it as GEi j,k Then, the higher number of the same analysis object GCi j,k , influence mean GTi j,k , and the highest carbon emissions per week GEi j,k Substitute the following formula to calculate the carbon emission value TUEi of the corresponding analysis object: j,k :

[0081]

[0082] Among them, TUEi j,k represents the carbon emission value of the kth analysis object in the jth user type in the i-th region, GCi j,k ,GTi j,k and GEi j,k They represent the number of excesses, the impact mean, and the highest carbon emissions in a single week of the kth analysis object in the jth user type in the i-th region, as well as They respectively represent the preset number threshold, the preset impact mean threshold and the preset maximum carbon emission threshold in a single week for the kth analysis object in the jth user type in the ith region, and b1, b2 and b3 respectively represent the preset weight factor for the number above, the preset weight factor for the impact mean and the preset weight factor for the maximum carbon emission in a single week.

[0083] The carbon emission anomaly value indicates the abnormal degree of carbon emissions of the analyzed object within seven weeks. Based on the calculated carbon emission anomaly values ​​of each analyzed object, it can be used to evaluate users with large carbon emission differences. Large carbon emission anomaly values ​​indicate that users have significant differences in carbon emissions during the analysis period. In addition, the carbon emission anomaly value can be used to understand the trend of improvement or deterioration of the analyzed object in carbon emissions, which helps to evaluate and monitor the dynamic changes of carbon emissions and take appropriate measures to manage and improve them in a timely manner.

[0084] contrast Figure 3The length between two adjacent groups of high-outgoing lines of the analysis object is different in length. The angle between the two groups of high-outgoing lines is calculated. This angle is called the high-average angle. The high-average angle can be used to understand the changing trend between data points. When the length of the previous high-outgoing line is longer than the length of the subsequent high-outgoing line, and the corresponding numerical points of the two groups of high-outgoing lines are connected by a change line, the angle between the change line and the horizontal line is directly calculated to obtain the high-average angle 1; if the corresponding numerical points of the two groups of high-outgoing lines are not connected by a change line, the two groups of numerical points are connected to construct an angle line, and the angle between the angle line and the horizontal line is calculated to obtain the high-average angle 1;

[0085] When the length of the previous high-outline is shorter than that of the subsequent high-outline, and the two sets of corresponding numerical points of the high-outline are connected by a change line, the angle between the change line and the horizontal line is directly calculated to obtain the second high-average angle; if the two sets of corresponding numerical points of the high-outline are not connected by a change line, the two sets of numerical points are connected to construct an angle line, and the angle between the angle line and the horizontal line is calculated to obtain the second high-average angle;

[0086] Add up the angle values ​​of all the high-average angles 1 of the analysis object to get the high-average total value 1, add up the angle values ​​of all the high-average angles 2 of the analysis object to get the high-average total value 2, and calculate the ratio of the high-average total value 2 to the high-average total value 1 to get the high-average ratio GBi of the corresponding analysis object j,k The height-to-average ratio is used to measure the change in the height-to-average angle of the analysis object. If the height-to-average ratio is close to 1, it means that the angle change between two adjacent groups of height lines is small; if the height-to-average ratio is far from 1, it means that the angle change between two adjacent groups of height lines is large.

[0087] It should be noted that by analyzing the changes in carbon emissions of the corresponding objects that are above the threshold line, it is possible to determine whether the user is showing an upward or downward trend during the process of high energy use, which further improves the accuracy and comprehensiveness of data analysis.

[0088] Step S4: determining the overall emission change index of all regions according to the emission change value of each analysis object and the preset emission change threshold of each user type in each region;

[0089] In a preferred embodiment, the overall emission change index of all regions is determined based on the emission change value of each analysis object and the preset emission change threshold of each user type in each region, including:

[0090] For each user type in each area, the emission variation values ​​of each analysis object in the user type are averaged to obtain the emission mean value of the corresponding user type;

[0091] Calculate the absolute difference between the emission variation value of each analysis object in the user type and the emission mean value of the corresponding user type;

[0092] From the absolute differences, select the target absolute differences that meet the preset value range;

[0093] The emission change values ​​corresponding to the target absolute difference are averaged to obtain the emission change assessment index for the corresponding user type;

[0094] Based on the emission change assessment index of each user type in each region and the preset emission change threshold of each user type in each region, the overall emission change index of all regions is calculated by the following formula:

[0095]

[0096] Among them, ZDG represents the overall emission change index, P represents the number of regions, U represents the number of user types, and PFBi j represents the emission change assessment index of the jth user type in the i-th region, represents the preset emission change threshold of the jth user type in the i-th region, hi j Represents the preset weight factor of the emission change assessment index of the j-th user type in the ith region.

[0097] For step S4, the emission variable LBi of each analysis object in each user type in each area obtained in step S3 is j,k For each user type in each area, the emission variation values ​​of each analysis object in the user type are averaged to obtain the emission mean of the corresponding user type. The emission mean represents the average level of emission variation values ​​of the user type in the area, which can be used to compare the emission levels of different user types or to evaluate the emission differences between different areas.

[0098] Next, calculate the absolute difference between the emission variation of each analysis object in the user type and the emission mean of the corresponding user type. This absolute difference represents the degree of deviation between the emission variation of each analysis object and the overall emission mean of the user type. Then, according to the preset value range, select the target absolute difference that meets the preset value range from the absolute difference. The purpose of this is to remove those deviations that are too large and too small, so as to more accurately evaluate the change in emissions. Finally, calculate the mean of the emission variation corresponding to the target absolute difference to obtain the emission change assessment index PFBi for the corresponding user type. j This emission change assessment index represents the average value of emission variation within a set differential range, and can be used to understand the emission variation trend of a specific user type within a set differential range, as well as to compare emission changes between different user types.

[0099] The emission change assessment index PFBi for each user type in each area is obtainedj Substitute into the following formula to calculate the overall emission change index ZDG for all regions:

[0100]

[0101] where ZDG represents the overall emission change index, P represents the number of regions, U represents the number of user types, and PFBi j represents the emission change assessment index for the j-th user type in the i-th region, represents the preset emission change threshold for the j-th user type in the i-th region, and hi j represents the preset weight factor of the emission change assessment index for the j-th user type in the i-th region. Through the overall emission change index, the emission changes of the entire system are comprehensively evaluated to understand the change trend of the overall emissions and to compare the emission changes between different regions.

[0102] When the overall emission change index ZDG exceeds the preset threshold γ, warnings of different levels are issued, and different levels of warnings correspond to different emergency measures. For example: when γ ≤ ZDG ≤ 1.2γ, a first-level warning is issued, and users are reminded to pay attention to reducing emissions, and suggestions and guidelines for energy conservation and emission reduction are provided. At the same time, carbon emission data analysis and assessment are carried out to find potential for further emission reduction; when 1.2γ ≤ ZDG ≤ 1.7γ, a second-level warning is issued, restricting the development of high-carbon emission industries, and at the same time encouraging the development of green industries; when 1.7γ < ZDG, a third-level warning is issued, restricting the production or operation of high-carbon emission industries, etc.

[0103] It should be noted that the preset emission change thresholds for different regions and different user types represent the allowed emission change thresholds set for different regions and user types under specific carbon emission rules. This value is set according to the target requirements of the carbon emission rules in different regions. For example: the emission change threshold for residential user types in Region A is 10 units, and the emission change assessment index for residential user types in Region A calculated based on the data of the first seven weeks is 5 units. Then, the ratio of the emission change assessment index to the preset emission change threshold can be calculated as 5 / 10 = 0.5. When the emission change assessment index is smaller, it means that the actual emission change is closer to or lower than the preset threshold, which means that the emission reduction effect is better. Generally speaking, if the emission change assessment indexes of all regions and user types are smaller, then the overall emission change index is also smaller, indicating a better overall emission reduction effect.

[0104] The preset weight factor hi of the emission change assessment index for different regions and different user types j, represents the proportion of carbon emissions in different regions in the global carbon emissions. This value is set according to factors such as the economic development level, population size, and energy consumption of each region. For example, the weight factor of the emission change assessment index of residential user types in region A is 1, and the weight factor of the emission change assessment index of commercial user types in region A is 3, which means that the proportion of carbon emissions of commercial user types in the carbon emissions of region A is greater than that of residential user types. By giving greater weights to some user types in some regions, the carbon emission contributions of different regions and user types can be more accurately reflected in the emission change assessment index. At the same time, such weight settings help motivate relevant departments to pay more attention to and attach more importance to carbon emission reduction, and encourage them to take more environmental protection measures and green technologies to reduce carbon emissions and promote sustainable economic development.

[0105] Step S5: determining the carbon emission anomaly assessment index of each analysis object according to the carbon emission anomaly value and high-to-average ratio of each analysis object, and the preset carbon emission anomaly threshold and the preset high-to-average ratio threshold of each user type in each area;

[0106] In a preferred embodiment, the carbon emission anomaly assessment index of each analysis object is determined according to the carbon emission anomaly value and high-to-average ratio of each analysis object, and the preset carbon emission anomaly threshold and the preset high-to-average ratio threshold of each user type in each area, including:

[0107] According to the carbon emission anomaly value and high-average ratio of each analysis object, as well as the preset carbon emission anomaly threshold and preset high-average ratio threshold of each user type in each region, the carbon emission anomaly assessment index of the corresponding analysis object is calculated by the following formula:

[0108]

[0109] Among them, TYQi j,k TUEi represents the carbon emission anomaly evaluation index of the kth analysis object in the jth user type in the i-th region, j,k represents the carbon emission value of the kth analysis object in the jth user type in the i-th region, represents the preset carbon emission outlier threshold of the jth user type in the i-th region, cf1 represents the preset weight factor of the carbon emission outlier, GBi j,k GBi represents the high-to-average ratio of the kth analysis object in the jth user type in the i-th region, j t represents the preset high-to-average ratio threshold of the j-th user type in the ith area, and cf2 represents the preset weight factor of the high-to-average ratio.

[0110] For step S5, the preset carbon emission difference value threshold and the preset high-average ratio threshold of each user type in each area are obtained, and the carbon emission difference value TUEi of each analysis object obtained in step S3 is obtained. j,k and high mean ratio GBi j,k , the carbon emission abnormality assessment index TYQi corresponding to the analysis object is calculated by the formula j,k ,This index represents the degree of abnormal carbon emissions of the analyzed object, and is used to evaluate the carbon emissions of the analyzed object, which helps to understand which objects have a higher risk of abnormal carbon emissions.

[0111] Step S6: determining the regional emission anomaly assessment index of each region according to the carbon emission anomaly assessment index of each analysis object;

[0112] In a preferred embodiment, the regional emission anomaly assessment index of each region is determined according to the carbon emission anomaly assessment index of each analysis object, including:

[0113] For each user type in each region, the carbon emission anomaly assessment index of each analysis object in the user type is compared with the corresponding preset carbon emission anomaly threshold;

[0114] Filter out the carbon emission anomaly assessment index that is higher than the corresponding preset carbon emission anomaly threshold, and use it as the target carbon emission anomaly assessment index;

[0115] The ratio of the number of target carbon emission abnormality assessment indexes to the number of carbon emission abnormality assessment indexes is calculated to obtain the abnormal height ratio of the corresponding user type;

[0116] Calculate the difference between the target carbon emission anomaly assessment index and the corresponding preset carbon emission anomaly threshold to obtain the target carbon emission anomaly difference;

[0117] Calculate the average of the target carbon emission abnormal difference values ​​within the first preset difference range to obtain a difference average value;

[0118] Calculate the average of the target carbon emission abnormal difference in the second preset difference range to obtain a difference-average binary value; wherein the second preset difference range is higher than the first preset difference range;

[0119] Perform weighted calculation on the difference mean one value and the difference mean two value to obtain the abnormal high estimation value of the corresponding user type;

[0120] The emission anomaly value of the corresponding user type is calculated according to the anomaly ratio of the corresponding user type, the anomaly estimation of the corresponding user type, the preset anomaly ratio threshold of the corresponding user type and the preset anomaly estimation threshold of the corresponding user type;

[0121] For each region, the regional emission anomaly assessment index is calculated based on the emission anomaly values ​​of each user type in the region and the preset weight factors corresponding to the emission anomaly values ​​of each user type.

[0122] For step S6, for all analysis objects in each user type in each region, the carbon emission anomaly assessment index of all analysis objects is compared with the corresponding preset carbon emission anomaly threshold, and the carbon emission anomaly assessment index higher than the corresponding threshold is extracted, and the number of abnormal highs is counted to obtain the number of abnormal highs, and the ratio between the corresponding abnormal high number and the corresponding screening number is calculated to obtain the abnormal high ratio TFGi of each user type in each region. j ; Then, the difference between the carbon emission anomaly assessment index corresponding to the number of abnormal heights in each group and the corresponding threshold is calculated, and two difference value ranges Y1 and Y2 are set at the same time, where the range of Y2 is higher than the range of Y1, and each group of value ranges corresponds to a weight coefficient, and the weight coefficients are marked as V1 and V2, and V2>V1; each group of differences is matched to the corresponding value range, and the differences in the same value range are averaged to obtain the difference mean value COi j CTi j Then, the estimated value YKMi for each user type in each region is calculated by the following formula: j :

[0123]

[0124] Among them, YKMi j represents the outlier estimate of the jth user type in the i-th region.

[0125] It should be noted that the anomaly ratio represents the proportion of the number of analysis objects with higher risk of abnormal carbon emissions relative to the total number of screenings; the anomaly valuation is obtained by multiplying the difference with the weight and taking the weighted average of the calculation results of all analysis objects. It combines the size of the difference and the corresponding weight, and is used to comprehensively evaluate the degree of carbon emission anomaly in each region and each user type.

[0126] The height difference ratio TFGi corresponding to the user type in the current monitoring area j With unusually high valuations YKMi j Substitute the formula to calculate the emission outlier value PCVi for the corresponding area and user type j :

[0127]

[0128] Among them, PCVi j represents the emission outlier of the jth user type in the i-th region, TFGi j and YKMi jThey represent the abnormal height ratio and abnormal height estimation of the j-th user type in the i-th region, respectively. and The values ​​represent the preset abnormal height ratio threshold and the preset abnormal height estimation threshold of the jth user type in the ith region, respectively. fb1 and fb2 represent the preset weight factors of the abnormal height ratio and abnormal height estimation, respectively. The emission abnormal value is used to measure the abnormal carbon emission of a specific user type in a specific region, reflecting the abnormal degree and risk of carbon emission of this user type in this region.

[0129] Substitute the emission anomaly values ​​of different user types in the current monitoring area into the following formula to calculate the regional emission anomaly assessment index JYPi of the current monitoring area:

[0130]

[0131] Among them, JYPi represents the regional emission anomaly assessment index of the ith region, PCVi1, PCVi2 and PCVi3 represent the residential emission anomaly, commercial emission anomaly and industrial emission anomaly in the ith region, respectively, gh1, gh2 and gh3 represent the preset weight factors corresponding to residential users, commercial users and industrial users, respectively, and gh3>gh2>gh1>1. The regional emission anomaly assessment index is used to comprehensively evaluate the degree of carbon emission anomaly in the current monitoring area. It takes into account the emission anomaly and weights of different user types and reflects the overall carbon emission anomaly of the region.

[0132] Substitute the regional emission anomaly assessment index JYPi of each region into the following formula to calculate the overall emission anomaly assessment index ZTM of the total region:

[0133]

[0134] Among them, ZTM represents the overall emission anomaly assessment index of the total area, bv1 represents the preset weight factor of the regional emission anomaly assessment index, It represents the regional emission anomaly threshold index of each region. The overall emission anomaly assessment index is used to comprehensively assess the degree of carbon emission anomaly in the total region. It takes into account the emission anomaly assessment index and weights of different regions and reflects the carbon emission anomaly in the total region.

[0135] By calculating the regional emission anomaly assessment index JYPi for each region, the emission anomaly levels of different regions can be compared, which helps to understand which regions have abnormally high carbon emissions and which regions have abnormally low carbon emissions, thereby identifying areas that need focus and improvement.

[0136] In addition, the regional emission anomaly assessment index of each region can be compared, and the top three regions in the regional emission anomaly assessment index can be marked. At the same time, the regional emission anomaly assessment index corresponding to these three regions in the previous carbon analysis can be retrieved for comparison to determine whether the emission reduction effect of the previous time has improved or deteriorated. If the regional emission anomaly assessment index of these three regions has decreased compared with the previous time in this carbon analysis, that is, the emission anomaly level has decreased, then it can be judged as an improvement, indicating that the carbon emission situation in these regions has improved under the promotion of emission reduction measures, and the original emission reduction measures can continue to be implemented; on the contrary, if the regional emission anomaly assessment index of these three regions has increased compared with the previous time, that is, the emission anomaly level has increased, then it can be judged as a deterioration, indicating that the emission reduction efforts of these regions are insufficient, or they are facing some factors that increase carbon emissions, and the emission reduction plan needs to be adjusted.

[0137] The emission values ​​PCVi of each user type in each area j Substitute the following formulas to calculate the abnormal emission assessment index ERT1 for residential users, the abnormal emission assessment index ERT2 for commercial users, and the abnormal emission assessment index ERT3 for industrial users in the total area:

[0138]

[0139] Among them, ty1, ty2 and ty3 represent the preset weight factors of residential emission outliers, commercial emission outliers and industrial emission outliers respectively.

[0140] Step S7: The overall emission change index and the regional emission anomaly assessment index of each region are used as the carbon analysis result of the regional energy.

[0141] For step S7, the overall emission change index ZDG and the regional emission anomaly assessment index JYPi of each region are output as the carbon analysis results of regional energy. These indices can provide a comprehensive assessment of the carbon emission situation and the identification of abnormal situations.

[0142] By conducting carbon analysis regularly, a tracking and monitoring system can be established to analyze the changing trends of the overall emission change index. The overall emission change index can be used as an important basis for measuring and evaluating carbon emission reduction rules and measures, which will help to timely adjust and optimize carbon emission reduction rules.

[0143] At the same time, through the regional emission anomaly assessment index of each region, a preset threshold is set to identify areas with abnormal emissions. When the regional emission anomaly assessment index exceeds the preset threshold, these areas are set as key emission reduction areas. Specific emission reduction measures are taken for these areas, such as strengthening energy utilization efficiency, promoting clean energy technology, improving emission supervision and control measures, etc., to reduce their carbon emissions and improve the regional environmental quality.

[0144] Through the above analysis and measures, the present invention realizes regular carbon emission reduction tracking and monitoring, takes the overall emission change index as an important indicator for evaluating carbon emission reduction rules and measures, sets key emission reduction areas for areas with abnormal regional emissions, and takes corresponding emission reduction measures, which helps to optimize carbon emission reduction rules, promote the sustainable development of regional energy, and achieve sustainable economic growth while protecting the environment.

[0145] In a preferred embodiment, the carbon analysis method for regional energy further includes:

[0146] The carbon emission line graph of each analysis object, the emission variation value of each analysis object, the carbon emission difference value of each analysis object, the high-to-average ratio of each analysis object, the overall emission variation index, the carbon emission anomaly assessment index of each analysis object, and the regional emission anomaly assessment index of each region are transmitted to the display device so that the display device converts each data into a visual chart; the visual chart includes: a line chart or a bar chart.

[0147] In one embodiment of the present invention, the carbon emission line chart of each analysis object, emission variation value, carbon emission difference value, high-to-average ratio, overall emission change index, carbon emission anomaly assessment index of each analysis object and regional emission anomaly assessment index of each region obtained in the carbon analysis process are transmitted to a display device.

[0148] The display device displays a line graph of the carbon emissions of each analysis object, which can intuitively analyze the changing trend of the carbon emissions of each analysis object over time. Through the line graph, the carbon emissions between different analysis objects can be observed and compared, and possible trends and anomalies can be identified. At the same time, the display device will draw a bar graph of emission variation, carbon emission anomaly and high-to-average ratio to show the degree of emission variation, anomaly degree and high-to-average ratio between different analysis objects. The bar graph can more intuitively compare the differences between the analysis objects and identify possible anomalies and objects of focus.

[0149] Through these visualization charts, we can have a clearer understanding of the carbon emissions of regional energy, thus providing a basis for formulating carbon reduction rules and taking emission reduction measures.

[0150] In a preferred embodiment, the carbon analysis method for regional energy further includes:

[0151] Determine the carbon emission change level based on the range of the overall emission change index;

[0152] The regional carbon emission excess level of each region is determined based on the range of the regional emission anomaly assessment index of each region.

[0153] In one embodiment of the present invention, the overall emission change index is divided into the following levels: low emission change, medium emission change, and high emission change. In this embodiment, when the overall emission change index is between 0% and 5%, it can be classified as a low emission change level; when the overall emission change index is between 5% and 10%, it can be classified as a medium emission change level; when the overall emission change index exceeds 10%, it can be classified as a high emission change level. The carbon emission change level is determined according to the range of the overall emission change index.

[0154] The regional emission anomaly assessment index is divided into the following levels: low excess, medium excess, and high excess. In this embodiment, when the regional emission anomaly assessment index is between 0.1 and 0.5, it can be classified as a low excess level; when the regional emission anomaly assessment index is between 0.5 and 1.0, it can be classified as a medium excess level; when the regional emission anomaly assessment index exceeds 1.0, it can be classified as a high excess level. According to the range of the regional emission anomaly assessment index of each region, the regional carbon emission excess level of each region is determined.

[0155] By combining the carbon emission change level and the regional carbon emission excess level with visual charts, the overall emission changes and regional emission anomalies can be more intuitively understood. For example, color coding or different patterns can be used to represent different levels, making the data easier to understand and analyze. In this way, operators can quickly identify areas and objects that need to be focused on and measures taken to formulate corresponding carbon emission reduction rules and measures.

[0156] like Figure 4 As shown, based on the above method embodiment, a corresponding device embodiment is provided;

[0157] An embodiment of the present invention provides a carbon analysis device for regional energy, comprising: a carbon emission acquisition module, a carbon emission line graph construction module, a carbon emission data determination module, an overall emission change index determination module, a carbon emission anomaly assessment index determination module, a regional emission anomaly assessment index determination module, and a carbon analysis result output module;

[0158] The carbon emission acquisition module is used to obtain the carbon emission of each analysis object in each user type in each region for several weeks in history;

[0159] A carbon emission line graph construction module is used to construct a carbon emission line graph of each analysis object according to the carbon emission;

[0160] The carbon emission data determination module is used to determine the emission variation value, carbon emission difference value and high-average ratio of each analysis object according to the slope of each line segment in the carbon emission line graph of each analysis object, the abnormal threshold and the abnormal threshold high line of each line segment;

[0161] An overall emission change index determination module is used to determine the overall emission change index of all areas according to the emission change value of each analysis object and the preset emission change threshold of each user type in each area;

[0162] A carbon emission anomaly assessment index determination module is used to determine the carbon emission anomaly assessment index of each analysis object according to the carbon emission anomaly value and high-to-average ratio of each analysis object, as well as a preset carbon emission anomaly threshold and a preset high-to-average ratio threshold for each user type in each region;

[0163] A regional emission anomaly assessment index determination module is used to determine the regional emission anomaly assessment index of each region according to the carbon emission anomaly assessment index of each analysis object;

[0164] The carbon analysis result output module is used to output the overall emission change index and the regional emission anomaly assessment index of each region as the carbon analysis result of regional energy.

[0165] It can be understood that the above-mentioned device item embodiments correspond to the method item embodiments of the present invention, and can implement the carbon analysis method for regional energy provided by any of the above-mentioned method item embodiments of the present invention.

[0166] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art may understand and implement the present invention without creative work.

[0167] Based on the above-mentioned embodiment of the carbon analysis method for regional energy, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the carbon analysis method for regional energy of any embodiment of the present invention is implemented.

[0168] Exemplarily, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more module elements may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the terminal device.

[0169] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0170] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0171] Based on the above method embodiment, another embodiment is provided: another embodiment of the present invention provides a computer-readable storage medium, including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the carbon analysis method for regional energy described in any one of the above method embodiments of the present invention.

[0172] Wherein, the module / unit integrated with the carbon analysis device / terminal equipment for regional energy, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0173] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A carbon analysis method for regional energy, characterized in that: include: Obtain the carbon emissions of each analysis object in each user type in each region for several weeks in history; Construct a carbon emission line chart for each analysis object based on carbon emissions; According to the slope of each line segment in the carbon emission line graph of each analysis object, the abnormal threshold and the abnormal threshold of each line segment above the line, the emission variation value, carbon emission difference value and high-average ratio of each analysis object are determined; Determine the overall emission change index for all regions based on the emission change value of each analysis object and the preset emission change threshold of each user type in each region; Determine the carbon emission anomaly assessment index for each analysis object based on the carbon emission anomaly value and high-average ratio of each analysis object, as well as the preset carbon emission anomaly threshold and preset high-average ratio threshold of each user type in each region; According to the carbon emission anomaly assessment index of each analysis object, determine the regional emission anomaly assessment index of each region; The overall emission change index and the regional emission anomaly assessment index of each region are used as the carbon analysis results of regional energy.

2. The carbon analysis method for regional energy according to claim 1, characterized in that: Also includes: The carbon emission line graph of each analysis object, the emission variation value of each analysis object, the carbon emission difference value of each analysis object, the high-to-average ratio of each analysis object, the overall emission variation index, the carbon emission anomaly assessment index of each analysis object, and the regional emission anomaly assessment index of each region are transmitted to a display device so that the display device converts each data into a visual chart; the visual chart includes: a line graph or a bar chart.

3. The carbon analysis method for regional energy according to claim 1, characterized in that: Also includes: Determining a carbon emission change level according to the range of the overall emission change index; The regional carbon emission exceeding standard level of each region is determined according to the range of the regional emission anomaly assessment index of each region.

4. The carbon analysis method for regional energy according to claim 1, characterized in that: Obtain the carbon emissions of each analysis object in each user type in each region for several weeks in history, including: Obtain the traditional energy consumption of each analysis object in each user type in each region for several weeks in history; For each analysis object of each user type in each region, the carbon emissions of the analysis object in the past several weeks are calculated based on the traditional energy consumption of the analysis object in the past several weeks and the corresponding carbon emission coefficient.

5. The carbon analysis method for regional energy according to claim 1, characterized in that: Based on the emission change values ​​of each analysis object and the preset emission change thresholds of each user type in each area, the overall emission change index of all areas is determined, including: For each user type in each area, the emission variation values ​​of each analysis object in the user type are averaged to obtain the emission mean value of the corresponding user type; Calculate the absolute difference between the emission variation value of each analysis object in the user type and the emission mean value of the corresponding user type; Filtering out a target absolute difference value that meets a preset value range from the absolute differences; Calculate the mean of the emission change values ​​corresponding to the target absolute difference to obtain the emission change assessment index of the corresponding user type; Based on the emission change assessment index of each user type in each region and the preset emission change threshold of each user type in each region, the overall emission change index of all regions is calculated by the following formula: Among them, ZDG represents the overall emission change index, P represents the number of regions, U represents the number of user types, and PFBi j represents the emission change assessment index of the jth user type in the i-th region, represents the preset emission change threshold of the jth user type in the i-th region, hi j Represents the preset weight factor of the emission change assessment index of the j-th user type in the ith region.

6. The carbon analysis method for regional energy according to claim 1, characterized in that: According to the carbon emission anomaly value and high-average ratio of each analysis object, as well as the preset carbon emission anomaly threshold and preset high-average ratio threshold of each user type in each region, the carbon emission anomaly assessment index of each analysis object is determined, including: According to the carbon emission anomaly value and high-average ratio of each analysis object, as well as the preset carbon emission anomaly threshold and preset high-average ratio threshold of each user type in each region, the carbon emission anomaly assessment index of the corresponding analysis object is calculated by the following formula: Among them, TYQi j，k TUEi represents the carbon emission anomaly evaluation index of the kth analysis object in the jth user type in the i-th region, j，k represents the carbon emission value of the kth analysis object in the jth user type in the i-th region, represents the preset carbon emission outlier threshold of the jth user type in the i-th region, cf1 represents the preset weight factor of the carbon emission outlier, GBi j，k represents the high-to-average ratio of the kth analysis object in the jth user type in the i-th region, represents the preset high-to-average ratio threshold of the j-th user type in the ith area, and cf2 represents the preset weight factor of the high-to-average ratio.

7. The carbon analysis method for regional energy according to claim 1, characterized in that: According to the carbon emission anomaly assessment index of each analysis object, the regional emission anomaly assessment index of each region is determined, including: For each user type in each region, the carbon emission anomaly assessment index of each analysis object in the user type is compared with the corresponding preset carbon emission anomaly threshold; Filter out the carbon emission anomaly assessment index that is higher than the corresponding preset carbon emission anomaly threshold, and use it as the target carbon emission anomaly assessment index; The ratio of the number of target carbon emission abnormality assessment indexes to the number of carbon emission abnormality assessment indexes is calculated to obtain the abnormal height ratio of the corresponding user type; Calculate the difference between the target carbon emission anomaly assessment index and the corresponding preset carbon emission anomaly threshold to obtain the target carbon emission anomaly difference; Calculate the average of the target carbon emission abnormal difference values ​​within the first preset difference range to obtain a difference average value; Calculate the average of the target carbon emission abnormal difference in the second preset difference range to obtain a difference-average binary value; wherein the second preset difference range is higher than the first preset difference range; Performing weighted calculation on the difference mean one value and the difference mean two value to obtain an abnormal height estimation value of the corresponding user type; Calculate the emission anomaly value of the corresponding user type according to the anomaly ratio of the corresponding user type, the anomaly estimation of the corresponding user type, the preset anomaly ratio threshold of the corresponding user type and the preset anomaly estimation threshold of the corresponding user type; For each region, the regional emission anomaly assessment index is calculated based on the emission anomaly values ​​of each user type in the region and the preset weight factors corresponding to the emission anomaly values ​​of each user type.

8. A carbon analysis device for regional energy, characterized in that: include: A carbon emission acquisition module, a carbon emission line graph construction module, a carbon emission data determination module, an overall emission change index determination module, a carbon emission anomaly assessment index determination module, a regional emission anomaly assessment index determination module, and a carbon analysis result output module; The carbon emission acquisition module is used to acquire the carbon emission of each analysis object in each user type in each region for several weeks in history; The carbon emission line graph construction module is used to construct a carbon emission line graph of each analysis object according to the carbon emission; The carbon emission data determination module is used to determine the emission variation value, carbon emission difference value and high-to-average ratio of each analysis object according to the slope of each line segment in the carbon emission line graph of each analysis object, the abnormal threshold and the abnormal threshold of each line segment above the line; The overall emission change index determination module is used to determine the overall emission change index of all areas according to the emission change value of each analysis object and the preset emission change threshold of each user type in each area; The carbon emission anomaly assessment index determination module is used to determine the carbon emission anomaly assessment index of each analysis object according to the carbon emission anomaly value and high-to-average ratio of each analysis object, and the preset carbon emission anomaly value threshold and the preset high-to-average ratio threshold of each user type in each area; The regional emission anomaly assessment index determination module is used to determine the regional emission anomaly assessment index of each region according to the carbon emission anomaly assessment index of each analysis object; The carbon analysis result output module is used to use the overall emission change index and the regional emission anomaly assessment index of each region as the carbon analysis result of regional energy.

9. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the carbon analysis method for regional energy according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: include: A stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the carbon analysis method for regional energy according to any one of claims 1 to 7.

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