Urban carbon emission detection system and method based on power big data

Abstract

The application provides a kind of city carbon emission detection system and method based on electric power big data, the system includes: data acquisition module, obtains the electric power big data including power generation data and power consumption data;Data analysis module generates power generation curve chart and power consumption curve chart;Image finishing module, the point of limit in the power generation curve chart and power consumption curve chart is cut and reconstructed, generates power generation estimation chart and power consumption estimation chart;Image segmentation module, the power generation estimation chart and power consumption estimation chart are segmented into multiple power generation curve segments and power consumption curve segments according to the same curve taking shape rule;Feature extraction module, feature data is extracted, and constitutes power generation data feature set and power consumption data feature set;Carbon emission estimation module, calculate power generation side carbon emission and power consumption carbon emission.The application assesses the carbon emission index of a region through the influence of power generation end and power consumption end on carbon emission, realizes the reliability of carbon emission monitoring and increases the accuracy of carbon emission monitoring.

Description

Technical Field

[0001] This application seeks protection for carbon emission monitoring technology, and particularly relates to an urban carbon emission detection system based on big data of electricity consumption. This application also provides a method for urban carbon emission detection based on big data of electricity consumption. Background Technology

[0002] In my country, carbon emissions from electricity production are one of the main contributors to carbon emissions, accounting for more than 39% of the country's total carbon emissions. However, at the same time, electricity will be the main source of energy growth and is closely related to the national economy and people's livelihood.

[0003] The environmental impact of carbon emissions is obvious, making urban carbon emission monitoring a crucial environmental indicator. Currently, carbon emission monitoring is mainly conducted in two ways: monitoring carbon emissions at the power generation end and monitoring carbon emissions at the power consumption end. However, since carbon emission monitoring is primarily conducted within a limited area, neither method can accurately assess the amount of carbon emissions, leading to distorted monitoring data. Summary of the Invention

[0004] To address one or more of the technical problems described above, this application provides a city carbon emission detection system based on big data of electricity. This application also provides a method for detecting city carbon emissions based on big data of electricity.

[0005] This application provides a city carbon emission detection system based on big data of electricity, including:

[0006] The data acquisition module is used to acquire big data on electricity, including power generation and consumption data.

[0007] The data analysis module is used to generate power generation curves and power consumption curves based on the power big data and the corresponding time periods.

[0008] The image trimming module is used to cut and reconstruct the excess points in the power generation curve and power consumption curve according to preset rules, and generate the power generation estimation map and power consumption estimation map.

[0009] The image segmentation module is used to segment the power generation estimation map and the power generation estimation map into multiple power generation curve segments and power consumption curve segments according to the same curve shaping rules;

[0010] The feature extraction module is used to extract the feature data of each of the power generation curve segments and power consumption curve segments, and form a power generation data feature set and a power consumption data feature set.

[0011] The carbon emission calculation module is used to calculate the carbon emissions from power generation and the carbon emissions from electricity consumption based on the power generation data feature set and the electricity consumption data feature set, respectively, and multiply them by their respective influence factors to calculate the final carbon emissions.

[0012] Optionally, the preset rules include:

[0013] The curves on both sides of the over-limit point are divided into ordinary segments, trimmed segments, and reconstructed segments;

[0014] After replacing the reconstructed segment with a sine curve, the curvature of the trimmed segment is modified and then connected to the ordinary segment.

[0015] Optionally, the curve selection rules include:

[0016] Determine the magnitude of the curve's rise and fall, as well as the duration of the rise and fall;

[0017] Set time period threshold and rise / fall range threshold respectively. When the time period exceeds the time period threshold and rise / fall range threshold, set the corresponding curve segment as the power generation curve segment or the power consumption curve segment.

[0018] Optionally, the power generation data characteristics and power consumption data characteristics are average power.

[0019] Optionally, the time period for the power generation estimation map and the power consumption estimation map is one day.

[0020] This application also provides a method for detecting urban carbon emissions based on big data on electricity, including:

[0021] Acquire big data on electricity, including power generation and consumption data;

[0022] Based on the power big data and the corresponding time, power generation curves and power consumption curves are generated respectively.

[0023] According to preset rules, the excess points in the power generation curve and power consumption curve are cut and reconstructed to generate power generation estimation map and power consumption estimation map;

[0024] The power generation estimation map and the power generation estimation map are divided into multiple power generation curve segments and power consumption curve segments according to the same curve shaping rules;

[0025] The feature data of each of the power generation curve segments and power consumption curve segments are extracted and combined to form a power generation data feature set and a power consumption data feature set.

[0026] The carbon emissions from power generation and the carbon emissions from electricity consumption are calculated based on the power generation data feature set and the electricity consumption data feature set, respectively, and then multiplied by their respective influence factors to calculate the final carbon emissions.

[0027] Optionally, the preset rules include:

[0028] The curves on both sides of the over-limit point are divided into ordinary segments, trimmed segments, and reconstructed segments;

[0029] After replacing the reconstructed segment with a sine curve, the curvature of the trimmed segment is modified and then connected to the ordinary segment.

[0030] Optionally, the curve selection rules include:

[0031] Determine the magnitude of the curve's rise and fall, as well as the duration of the rise and fall;

[0032] Set time period threshold and rise / fall range threshold respectively. When the time period exceeds the time period threshold and rise / fall range threshold, set the corresponding curve segment as the power generation curve segment or the power consumption curve segment.

[0033] Optionally, the power generation data characteristics and power consumption data characteristics are average power.

[0034] Optionally, the time period for the power generation estimation map and the power consumption estimation map is one day.

[0035] The advantages of this application compared to the prior art are:

[0036] This application provides a city carbon emission detection system based on power big data, comprising: a data acquisition module for acquiring power big data containing power generation data and power consumption data; a data analysis module for generating power generation curves and power consumption curves according to the power big data and the time corresponding to the power big data; an image trimming module for cutting and reconstructing the excess points in the power generation curves and power consumption curves according to preset rules to generate power generation estimation maps and power consumption estimation maps; an image segmentation module for segmenting the power generation estimation maps and power consumption estimation maps into multiple power generation curve segments and power consumption curve segments according to the same curve shaping rules; a feature extraction module for extracting feature data from each power generation curve segment and power consumption curve segment and forming a power generation data feature set and a power consumption data feature set; and a carbon emission calculation module for calculating the carbon emissions on the power generation side and the carbon emissions on the power consumption side according to the power generation data feature set and the power consumption data feature set, and multiplying them by their respective influence factors to calculate the final carbon emissions. This application assesses a region's carbon emission indicators by jointly evaluating the impact of power generation and consumption on carbon emissions, thereby improving the reliability and accuracy of carbon emission monitoring. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the urban carbon emission detection system based on electricity big data in this application.

[0038] Figure 2 This is a flowchart of the over-limit power correction process in this application.

[0039] Figure 3 This is a flowchart of the urban carbon emission detection process based on big data of electricity in this application. Detailed Implementation

[0040] The following are examples of specific implementation processes provided to illustrate the technical solutions to be protected in this application. However, this application may also be implemented in other ways different from those described herein. Those skilled in the art can implement this application by different technical means under the guidance of the concept of this application. Therefore, this application is not limited to the specific embodiments below.

[0041] This application provides a city carbon emission detection system based on power big data, comprising: a data acquisition module for acquiring power big data containing power generation data and power consumption data; a data analysis module for generating power generation curves and power consumption curves according to the power big data and the time corresponding to the power big data; an image trimming module for cutting and reconstructing the excess points in the power generation curves and power consumption curves according to preset rules to generate power generation estimation maps and power consumption estimation maps; an image segmentation module for segmenting the power generation estimation maps and power consumption estimation maps into multiple power generation curve segments and power consumption curve segments according to the same curve shaping rules; a feature extraction module for extracting feature data from each power generation curve segment and power consumption curve segment and forming a power generation data feature set and a power consumption data feature set; and a carbon emission calculation module for calculating the carbon emissions on the power generation side and the carbon emissions on the power consumption side according to the power generation data feature set and the power consumption data feature set, and multiplying them by their respective influence factors to calculate the final carbon emissions. This application assesses a region's carbon emission indicators by jointly evaluating the impact of power generation and consumption on carbon emissions, thereby improving the reliability and accuracy of carbon emission monitoring.

[0042] Figure 1 This is a schematic diagram of the urban carbon emission detection system based on electricity big data in this application.

[0043] The data acquisition module 101 is used to acquire big data on electricity, including power generation data and power consumption data.

[0044] The power generation data refers to the amount of electricity generated, indicating the quantity of electricity produced. The electricity consumption data refers to the amount of electricity consumed, collected from a node in the power grid leading to the electrical equipment, such as a meter box or transformer.

[0045] The power generation and power consumption data can be collected by using voltage transformers and current transformers to collect voltage and current respectively, and finally the power generation or power consumption data can be calculated based on the voltage and current.

[0046] The data analysis module 102 is used to generate a power generation curve and a power consumption curve based on the power big data and the time corresponding to the power big data.

[0047] The power generation and power consumption data are collected in real time and marked on a plane coordinate system with time sequence as the horizontal axis and power consumption as the vertical axis to obtain power generation curves and power consumption curves.

[0048] Specifically, the horizontal axis of the planar coordinate system has a vertex representing the current time, which continuously increases over time. Preferably, the increase is at preset intervals, with each interval representing a specific time period. Preferably, the interval is the time acquisition period.

[0049] The image trimming module 103 is used to cut and reconstruct the excess points in the power generation curve and power consumption curve according to preset rules, and generate the power generation estimation map and power consumption estimation map.

[0050] The power generation curve and the power consumption curve are power consumption graphs generated based on the same time interval. Therefore, when the peak of the curve exceeds or falls below the power consumption limit range, it can be judged as exceeding the limit. The peak of the curve that exceeds the power consumption range is the over-limit power.

[0051] Figure 2 This is a flowchart of the over-limit power correction process in this application.

[0052] Please refer to Figure 2 As shown, S201 determines the power range, which refers to a preset range of power generation data or power consumption data.

[0053] S202 reads the over-limit points that exceed the power range and deletes the over-limit points and the curves outside the power range on both sides of the over-limit power.

[0054] S203 sets a sine function, such that the curve, compared to the sine function, sets the curvature at the intersection point, and corrects the intersection point to a smooth curvature.

[0055] The sine function is as follows:

[0056]

[0057] Wherein, A is the size of the power range, B is the starting point of cutting the curve, and C is the ending point of cutting the curve.

[0058] The image segmentation module 104 is used to segment the power generation estimation map and the power generation estimation map into multiple power generation curve segments and power consumption curve segments according to the same curve shaping rules.

[0059] The power generation curve segment and the power consumption curve segment are segmented according to specific needs. In this application, the power generation curve segment and the power consumption curve segment are segmented according to a preset time length. After the curve is divided into multiple curve segments, each curve segment is regarded as a data.

[0060] The feature extraction module 105 is used to extract the feature data of each of the power generation curve segments and power consumption curve segments, and form a power generation data feature set and a power consumption data feature set.

[0061] The feature data refers to data that can represent the actual power generation curve segment and power consumption curve segment. Specifically, it can be represented using various methods, such as calculating the area of ​​the curve segment in the coordinate system or calculating the average value of the curve segment. Then, all the feature data is combined into a power generation data feature set and a power consumption data feature set.

[0062] The carbon emission calculation module 106 is used to calculate the carbon emissions on the power generation side and the carbon emissions on the power consumption side based on the power generation data feature set and the power consumption data feature set, respectively, and multiply them by their respective influence factors to calculate the final carbon emissions.

[0063] Specifically, the feature data are summed to obtain the feature data sum over a period of time, including the power generation data feature data sum and the power consumption data feature data sum.

[0064] Based on the power generation data characteristics and the power consumption data characteristics, calculate the carbon emissions on the power generation side and the carbon emissions on the power consumption side, respectively.

[0065] Based on the carbon emissions from both the power generation and consumption sides, corresponding influence factors are assigned to each, and the average value is calculated to obtain the final carbon emission data. Here, the influence factor represents the accuracy of the detected carbon emission values.

[0066] This application also provides a method for detecting urban carbon emissions based on big data of electricity. This method assesses the carbon emission indicators of a region by jointly evaluating the impact of power generation and consumption on carbon emissions, thereby improving the reliability and accuracy of carbon emission monitoring.

[0067] Figure 3 This is a flowchart of the urban carbon emission detection process based on big data of electricity in this application.

[0068] Please refer to Figure 3 As shown, S101 acquires big data on electricity, including power generation data and power consumption data.

[0069] The power generation data refers to the amount of electricity generated, indicating the quantity of electricity produced. The electricity consumption data refers to the amount of electricity consumed, collected from a node in the power grid leading to the electrical equipment, such as a meter box or transformer.

[0070] The power generation and power consumption data can be collected by using voltage transformers and current transformers to collect voltage and current respectively, and finally the power generation or power consumption data can be calculated based on the voltage and current.

[0071] Please refer to Figure 1 As shown, S302 generates a power generation curve and a power consumption curve based on the power big data and the time corresponding to the power big data.

[0072] The power generation and power consumption data are collected in real time and marked on a plane coordinate system with time sequence as the horizontal axis and power consumption as the vertical axis to obtain power generation curves and power consumption curves.

[0073] Specifically, the horizontal axis of the planar coordinate system has a vertex representing the current time, which continuously increases over time. Preferably, the increase is at preset intervals, with each interval representing a specific time period. Preferably, the interval is the time acquisition period.

[0074] Please refer to Figure 3 As shown, S303 cuts and reconstructs the over-limit points in the power generation curve and power consumption curve according to preset rules to generate a power generation estimation map and a power consumption estimation map.

[0075] The power generation curve and the power consumption curve are power consumption graphs generated based on the same time interval. Therefore, when the peak of the curve exceeds or falls below the power consumption limit range, it can be judged as exceeding the limit. The peak of the curve that exceeds the power consumption range is the over-limit power.

[0076] Figure 2 This is a flowchart of the over-limit power correction process in this application.

[0077] Please refer to Figure 2 As shown, S201 determines the power range, which refers to a preset range of power generation data or power consumption data.

[0078] S202 reads the over-limit points that exceed the power range and deletes the over-limit points and the curves outside the power range on both sides of the over-limit power.

[0079] S203 sets a sine function, such that the curve, compared to the sine function, sets the curvature at the intersection point, and corrects the intersection point to a smooth curvature.

[0080] The sine function is as follows:

[0081]

[0082] Wherein, A is the size of the power range, B is the starting point of cutting the curve, and C is the ending point of cutting the curve.

[0083] Please refer to Figure 3 As shown, S304 divides the power generation estimation diagram and the power generation estimation diagram into multiple power generation curve segments and power consumption curve segments according to the same curve shaping rules.

[0084] The power generation curve segment and the power consumption curve segment are segmented according to specific needs. In this application, the power generation curve segment and the power consumption curve segment are segmented according to a preset time length. After the curve is divided into multiple curve segments, each curve segment is regarded as a data.

[0085] Please refer to Figure 3 As shown, S305 extracts the feature data of each of the power generation curve segments and power consumption curve segments, and forms a power generation data feature set and a power consumption data feature set.

[0086] The feature data refers to data that can represent the actual power generation curve segment and power consumption curve segment. Specifically, it can be represented using various methods, such as calculating the area of ​​the curve segment in the coordinate system or calculating the average value of the curve segment. Then, all the feature data is combined into a power generation data feature set and a power consumption data feature set.

[0087] Please refer to Figure 3 As shown in S306, the carbon emissions from power generation and the carbon emissions from electricity consumption are calculated based on the power generation data feature set and the electricity consumption data feature set, respectively, and then multiplied by their respective influence factors to calculate the final carbon emissions.

[0088] Specifically, the feature data are summed to obtain the feature data sum over a period of time, including the power generation data feature data sum and the power consumption data feature data sum.

[0089] Based on the power generation data characteristics and the power consumption data characteristics, calculate the carbon emissions on the power generation side and the carbon emissions on the power consumption side, respectively.

[0090] Based on the carbon emissions from both the power generation and consumption sides, corresponding influence factors are assigned to each, and the average value is calculated to obtain the final carbon emission data. Here, the influence factor represents the accuracy of the detected carbon emission values.

[0091] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0092] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A city carbon emission detection system based on electricity big data, characterized in that, include: The data acquisition module is used to acquire big data on electricity, including power generation and consumption data. The data analysis module is used to generate power generation curves and power consumption curves based on the power big data and the corresponding time periods. The image trimming module is used to cut and reconstruct the over-limit points in the power generation curve and power consumption curve according to preset rules, and generate power generation estimation map and power consumption estimation map; the preset rules include: dividing the curves on both sides of the over-limit point into ordinary segment, trimmed segment and reconstructed segment; after replacing the reconstructed segment with a sine curve, the curvature of the trimmed segment is modified and connected to the ordinary segment; The image segmentation module is used to segment the power generation estimation map and the power consumption estimation map into multiple power generation curve segments and power consumption curve segments according to the same curve shaping rules. The curve shaping rules include: judging the curve rise and fall amplitude and the length of the rise and fall time period; setting time period thresholds and rise and fall amplitude thresholds respectively; when the time period exceeds the time period thresholds and rise and fall amplitude thresholds, the corresponding curve segment is set as a power generation curve segment or a power consumption curve segment. The feature extraction module is used to extract the feature data of each of the power generation curve segments and power consumption curve segments, and form a power generation data feature set and a power consumption data feature set. The carbon emission calculation module is used to calculate the carbon emissions from power generation and the carbon emissions from electricity consumption based on the power generation data feature set and the electricity consumption data feature set, respectively, and multiply them by their respective influence factors to calculate the final carbon emissions; the power generation data feature and the electricity consumption data feature are average power.

2. The urban carbon emission detection system based on power big data according to claim 1, characterized in that, The time period for the power generation estimation map and the power consumption estimation map is one day.

3. A method for detecting urban carbon emissions based on big data of electricity, characterized in that, include: Acquire big data on electricity, including power generation and consumption data; Based on the power big data and the corresponding time, power generation curves and power consumption curves are generated respectively. According to preset rules, the over-limit points in the power generation curve and power consumption curve are cut and reconstructed to generate power generation estimation map and power consumption estimation map; the preset rules include: dividing the curves on both sides of the over-limit point into ordinary segment, trimmed segment and reconstructed segment; after replacing the reconstructed segment with a sine curve, the curvature of the trimmed segment is modified and connected to the ordinary segment. The power generation estimation map and the power consumption estimation map are divided into multiple power generation curve segments and power consumption curve segments according to the same curve shaping rules; the curve shaping rules include: judging the curve rise and fall amplitude and the length of the rise and fall time period; setting time period thresholds and rise and fall amplitude thresholds respectively; when the time period exceeds the time period threshold and rise and fall amplitude threshold, the corresponding curve segment is set as a power generation curve segment or a power consumption curve segment. The feature data of each of the power generation curve segments and power consumption curve segments are extracted and combined to form a power generation data feature set and a power consumption data feature set. The carbon emissions from power generation and the carbon emissions from electricity consumption are calculated based on the power generation data feature set and the electricity consumption data feature set, respectively, and then multiplied by their respective influence factors to calculate the final carbon emissions; the power generation data feature and the electricity consumption data feature are average power.

4. The urban carbon emission detection method based on power big data according to claim 3, characterized in that, The time period for the power generation estimation map and the power consumption estimation map is one day.

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