CN115249089A - Urban carbon emission detection system and method based on electric power big data - Google Patents
Urban carbon emission detection system and method based on electric power big data Download PDFInfo
- Publication number
- CN115249089A CN115249089A CN202210770205.2A CN202210770205A CN115249089A CN 115249089 A CN115249089 A CN 115249089A CN 202210770205 A CN202210770205 A CN 202210770205A CN 115249089 A CN115249089 A CN 115249089A
- Authority
- CN
- China
- Prior art keywords
- power
- power generation
- data
- curve
- carbon emission
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/80—Management or planning
- Y02P90/84—Greenhouse gas [GHG] management systems
Abstract
The application provides a city carbon emission detection system and method based on electric power big data, and the system comprises: the data acquisition module is used for acquiring power big data comprising power generation data and power utilization data; the data analysis module generates a power generation curve graph and a power utilization curve graph; the image trimming module cuts and reconstructs the overrun points in the power generation curve graph and the power utilization curve graph to generate a power generation estimation graph and a power utilization estimation graph; the image segmentation module is used for segmenting the power generation estimation graph and the power generation estimation graph into a plurality of power generation curve segments and power utilization curve segments according to the same curve modeling rule; the characteristic extraction module extracts the characteristic data and forms a power generation data characteristic set and a power utilization data characteristic set; and the carbon emission measuring and calculating module is used for calculating the carbon emission of the power generation side and the carbon emission measured by electricity. According to the carbon emission monitoring method and device, the carbon emission index of one area is jointly evaluated through the influence of the power generation end and the power utilization end on carbon emission, so that the reliability of carbon emission monitoring is realized, and the accuracy of carbon emission monitoring is improved.
Description
Technical Field
The application requests protection carbon emission monitoring technology, especially relates to a city carbon emission detection system based on electric power big data. The application further provides a city carbon emission detection method based on the electric power big data.
Background
In China, carbon emission caused by electricity production is one of main factors of carbon emission, and accounts for more than 39% of the total carbon emission in China, but at the same time, electricity is the main body of energy growth and is closely related to the national civilian life.
The influence of carbon emission on the environment is obvious, and how to monitor the urban carbon emission becomes an important environmental monitoring index. At present, carbon emission monitoring mainly goes on through two kinds of modes, one kind is the monitoring of electricity generation end carbon emission, and the monitoring of power consumption end carbon emission, and carbon emission mainly goes on to a limited region in fact, this just leads to no matter carry out carbon emission monitoring from the electricity generation end, still carries out carbon emission monitoring through the power consumption end, and all unable accurate aassessment carbon emission leads to the monitoring data distortion.
Disclosure of Invention
In order to solve one or more technical problems in the technical scheme, the application provides an urban carbon emission detection system based on electric power big data. The application also provides a city carbon emission detection method based on the electric power big data.
The application provides a city carbon discharge capacity detecting system based on electric power big data includes:
the data acquisition module is used for acquiring power big data containing power generation data and power utilization data;
the data analysis module is used for respectively generating a power generation curve graph and a power utilization curve graph according to the electric power big data and the time corresponding to the electric power big data;
the image trimming module is used for cutting and reconstructing the overrun points in the power generation curve graph and the power utilization curve graph according to a preset rule to generate a power generation estimation graph and a power utilization estimation graph;
the image segmentation module is used for segmenting the power generation estimation graph and the power generation estimation graph into a plurality of power generation curve segments and power utilization curve segments according to the same curve modeling rule;
the characteristic extraction module is used for extracting characteristic data of each power generation curve segment and each power utilization curve segment and forming a power generation data characteristic set and a power utilization data characteristic set;
and the carbon emission measuring and calculating module is used for respectively calculating the carbon emission of the power generation side and the carbon emission of the electricity utilization measuring according to the power generation data characteristic set and the electricity utilization data characteristic set, and respectively multiplying the carbon emission and the carbon emission by respective influence factors to calculate the final carbon emission.
Optionally, the preset rule includes:
dividing curves on two sides of the overrun point into a common segment, a trimming segment and a reconstruction segment;
and after the reconstruction segment is replaced by the sine curve, the reconstruction segment is connected to the ordinary segment by modifying the curvature of the trimming segment.
Optionally, the curve typing rule includes:
judging the lifting amplitude of the curve and the length of the lifting time period;
respectively setting a time period threshold value and a lifting amplitude threshold value, and setting the corresponding curve segment as a power generation curve segment or a power utilization curve segment after the time period exceeds the time period threshold value and the lifting amplitude threshold value.
Optionally, the power generation data characteristic and the power consumption data characteristic are average power.
Optionally, the time period of the power generation estimation map and the power utilization estimation map is one day.
The application also provides a city carbon emission detection method based on the electric power big data, which comprises the following steps:
acquiring power big data comprising power generation data and power utilization data;
generating a power generation curve graph and a power consumption curve graph respectively according to the power big data and the time corresponding to the power big data;
cutting and reconstructing the overrun points in the power generation curve graph and the power utilization curve graph according to a preset rule to generate a power generation estimation graph and a power utilization estimation graph;
dividing the power generation estimation diagram and the power generation estimation diagram into a plurality of power generation curve segments and power utilization curve segments according to the same curve modeling rule;
extracting the characteristic data of each power generation curve segment and each power utilization curve segment to form a power generation data characteristic set and a power utilization data characteristic set;
and respectively calculating the carbon emission of the power generation side and the carbon emission measured by electricity according to the power generation data characteristic set and the electricity utilization data characteristic set, and respectively multiplying the carbon emission and the carbon emission measured by the electricity utilization data characteristic set by respective influence factors to calculate the final carbon emission.
Optionally, the preset rule includes:
dividing the curves at two sides of the overrun point into a common section, a trimming section and a reconstruction section;
and after the reconstruction segment is replaced by the sine curve, the reconstruction segment is connected to the ordinary segment by modifying the curvature of the trimming segment.
Optionally, the curve typing rule includes:
judging the lifting amplitude of the curve and the length of the lifting time period;
respectively setting a time period threshold value and a lifting amplitude threshold value, and setting the corresponding curve segment as a power generation curve segment or a power utilization curve segment after the time period exceeds the time period threshold value and the lifting amplitude threshold value.
Optionally, the power generation data characteristic and the power consumption data characteristic are average power.
Optionally, the time period of the power generation estimation map and the power utilization estimation map is one day.
The application has the advantages over the prior art that:
the application provides a city carbon discharge capacity detecting system based on electric power big data includes: the data acquisition module is used for acquiring power big data containing power generation data and power utilization data; the data analysis module is used for respectively generating a power generation curve graph and a power utilization curve graph according to the electric power big data and the time corresponding to the electric power big data; the image trimming module is used for cutting and reconstructing the overrun points in the power generation curve graph and the power utilization curve graph according to a preset rule to generate a power generation estimation graph and a power utilization estimation graph; the image segmentation module is used for segmenting the power generation estimation graph and the power generation estimation graph into a plurality of power generation curve segments and power utilization curve segments according to the same curve modeling rule; the characteristic extraction module is used for extracting characteristic data of each power generation curve segment and each power utilization curve segment and forming a power generation data characteristic set and a power utilization data characteristic set; and the carbon emission measuring and calculating module is used for respectively calculating the carbon emission of the power generation side and the carbon emission of the electricity utilization measuring according to the power generation data characteristic set and the electricity utilization data characteristic set, and respectively multiplying the carbon emission and the carbon emission by respective influence factors to calculate the final carbon emission. According to the carbon emission monitoring method and device, the carbon emission index of one area is jointly evaluated through the influence of the power generation end and the power utilization end on carbon emission, so that the reliability of carbon emission monitoring is realized, and the accuracy of carbon emission monitoring is improved.
Drawings
Fig. 1 is a schematic diagram of a city carbon emission detection system based on electric power big data in the application.
Fig. 2 is a flowchart of the over-limit electricity correction in the present application.
Fig. 3 is a flow chart of city carbon emission detection based on electric big data in the application.
Detailed Description
The following is an example of specific implementation procedures provided for explaining the technical solutions to be protected in the present application in detail, but the present application may also be implemented in other ways than those described herein, and a person skilled in the art may implement the present application by using different technical means under the guidance of the idea of the present application, so that the present application is not limited by the following specific embodiments.
The application provides a city carbon discharge capacity detecting system based on electric power big data includes: the data acquisition module is used for acquiring power big data containing power generation data and power utilization data; the data analysis module is used for respectively generating a power generation curve graph and a power utilization curve graph according to the electric power big data and the time corresponding to the electric power big data; the image trimming module is used for cutting and reconstructing the overrun points in the power generation curve graph and the power utilization curve graph according to a preset rule to generate a power generation estimation graph and a power utilization estimation graph; the image segmentation module is used for segmenting the power generation estimation graph and the power generation estimation graph into a plurality of power generation curve segments and power utilization curve segments according to the same curve modeling rule; the characteristic extraction module is used for extracting characteristic data of each power generation curve segment and each power utilization curve segment and forming a power generation data characteristic set and a power utilization data characteristic set; and the carbon emission measuring and calculating module is used for respectively calculating the carbon emission of the power generation side and the carbon emission of the electricity utilization measuring according to the power generation data characteristic set and the electricity utilization data characteristic set, and respectively multiplying the carbon emission and the carbon emission by respective influence factors to calculate the final carbon emission. According to the carbon emission monitoring method and device, the carbon emission index of one area is jointly evaluated through the influence of the power generation end and the power utilization end on carbon emission, so that the reliability of carbon emission monitoring is realized, and the accuracy of carbon emission monitoring is improved.
Fig. 1 is a schematic diagram of a city carbon emission detection system based on electric power big data in the application.
The data acquisition module 101 is configured to acquire large power data including power generation data and power consumption data.
The power generation data is power generation amount data and indicates the amount of power generation. The electricity consumption data refers to electricity consumption data, and is collected from a node from a power grid to electric equipment, such as an electric meter box or a transformer.
And acquiring the power generation data and the power consumption data, wherein voltage and current can be acquired by adopting a voltage sensor and a current sympathetic device respectively, and finally the power generation data or the power consumption data can be obtained by calculating according to the voltage and the current.
And the data analysis module 102 is configured to generate a power generation curve graph and a power consumption curve graph according to the time corresponding to the power big data.
The power generation data and the power utilization data are collected in real time and are marked in a plane coordinate system with the time sequence as a horizontal axis and the power utilization size as a vertical axis, and a power generation curve graph and a power utilization curve graph are obtained.
Specifically, the horizontal axis of the planar coordinate system has a vertex, which is the current time and grows continuously with time. Preferably, the increment is according to a preset interval, and the time length of the interval is increased each time. Preferably, the interval is a period of time acquisition.
And the image trimming module 103 is used for cutting and reconstructing the overrun points in the power generation curve graph and the power utilization curve graph according to a preset rule to generate a power generation estimation graph and a power utilization estimation graph.
The power generation curve graph and the power utilization curve graph are generated based on the same interval time period, therefore, when the top point of the curve exceeds or is lower than a limited power interval, the curve can be judged to be over-limit, wherein the top point of the curve exceeding the power interval is over-limit power.
Fig. 2 is a flowchart of the over-limit electricity correction in the present application.
Referring to fig. 2, S201 determines an electric quantity interval, where the electric quantity interval refers to a preset range of power generation data or power consumption data.
S202, reading an over-limit point exceeding the electric quantity interval, and deleting the over-limit point and curves outside the electric quantity intervals at two sides of the over-limit electricity.
S203 sets a sine function such that the curve sets the curvature of the intersection compared to the sine function, and modifies the intersection to a smooth curvature.
Wherein the sine function is as follows:
wherein, a is the size of the electric quantity interval, B is the starting point time of cutting the curve, and C is the ending point time of cutting the curve.
And the image segmentation module 104 is used for segmenting the power generation estimation map and the power generation estimation map into a plurality of power generation curve segments and power utilization curve segments according to the same curve modeling rule.
The power generation curve segment and the power utilization curve segment are segmented according to specific requirements, in the application, the power generation curve segment and the power utilization curve segment are segmented according to preset time length, and after the curve is segmented into a plurality of curve segments, each curve segment is regarded as one piece of data.
And the feature extraction module 105 is configured to extract feature data of each of the power generation curve segments and the power consumption curve segments, and form a power generation data feature set and a power consumption data feature set.
The characteristic data refers to the fact that the characteristic data can represent the power generation curve segment and the power utilization curve segment, and can be expressed by a plurality of methods, such as calculating the area of the curve segment in a coordinate system, or calculating the average value of the curve segment, and the like. And then all the characteristic data are combined into a power generation data characteristic set and a power utilization data characteristic set.
And the carbon emission measuring and calculating module 106 is used for respectively calculating the carbon emission at the power generation side and the carbon emission measured by electricity according to the power generation data characteristic set and the electricity utilization data characteristic set, and respectively multiplying the carbon emission and the carbon emission measured by electricity by respective influence factors to calculate the final carbon emission.
Specifically, the feature data are summed to obtain a feature data sum in a period of time, wherein the feature data sum comprises power generation data feature data sum and power utilization data feature data sum.
And respectively solving the carbon emission on the power generation side and the carbon emission on the power utilization side based on the characteristic data sum of the power generation data and the characteristic data sum of the power utilization data.
And respectively giving corresponding influence factors based on the carbon emission of the power generation side and the carbon emission of the power utilization side, and then calculating an average value to obtain final carbon emission data. Wherein the influencing factor is a correct rate of the detected value of the carbon emission.
The method jointly evaluates the carbon emission index of a region through the influence of a power generation end and a power utilization end on carbon emission, realizes the reliability of carbon emission monitoring and increases the accuracy of carbon emission monitoring.
Fig. 3 is a flow chart of city carbon emission detection based on electric big data in the application.
Referring to fig. 3, in S101, power big data including power generation data and power consumption data is acquired.
The power generation data is power generation amount data and indicates the amount of power generation. The electricity consumption data refers to electricity consumption data, and is collected from a node from a power grid to electric equipment, such as an electric meter box or a transformer.
And acquiring the power generation data and the power consumption data, wherein a voltage sensor and a current sensor can be adopted to respectively acquire voltage and current, and finally, the power generation data or the power consumption data is obtained by calculation according to the voltage and the current.
Referring to fig. 1, in step S302, a power generation graph and a power consumption graph are respectively generated according to the time corresponding to the power big data and the power big data.
The power generation data and the power utilization data are collected in real time and are marked in a plane coordinate system with the time sequence as a horizontal axis and the power utilization size as a vertical axis, and a power generation curve graph and a power utilization curve graph are obtained.
Specifically, the horizontal axis of the planar coordinate system has a vertex, which is the current time and grows continuously with time. Preferably, the increment is according to a preset interval, and the time length of the interval is increased every time. Preferably, the interval is a period of time acquisition.
Referring to fig. 3, in S303, according to a preset rule, the overrun points in the power generation curve graph and the power consumption curve graph are cut and reconstructed to generate a power generation estimation graph and a power consumption estimation graph.
The power generation curve graph and the power utilization curve graph are generated based on the same interval time period, therefore, when the top point of the curve exceeds or is lower than a limited power interval, the curve can be judged to be over-limit, wherein the top point of the curve exceeding the power interval is over-limit power.
Fig. 2 is a flowchart of the over-limit electricity correction in the present application.
Referring to fig. 2, S201 determines an electric quantity interval, where the electric quantity interval refers to a preset range of power generation data or power consumption data.
S202, reading an over-limit point exceeding the electric quantity interval, and deleting the over-limit point and curves outside the electric quantity intervals at two sides of the over-limit electricity.
S203 sets a sine function such that the curve sets the curvature of the intersection compared to the sine function, and modifies the intersection to a smooth curvature.
Wherein the sine function is as follows:
wherein, a is the size of the electric quantity interval, B is the starting point moment of cutting the curve, and C is the ending point moment of cutting the curve.
Referring to fig. 3, S304 divides the power generation estimation map and the power generation estimation map into a plurality of power generation curve segments and power consumption curve segments according to the same curve extraction rule.
The power generation curve segment and the power utilization curve segment are segmented according to specific requirements, in the application, the power generation curve segment and the power utilization curve segment are segmented according to preset time length, and after the curve is segmented into a plurality of curve segments, each curve segment is regarded as one datum.
Referring to fig. 3, in S305, feature data of each of the power generation curve segments and the power consumption curve segments are extracted, and a power generation data feature set and a power consumption data feature set are formed.
The characteristic data refers to the fact that the characteristic data can represent the power generation curve segment and the power utilization curve segment, and can be expressed by a plurality of methods, such as calculating the area of the curve segment in a coordinate system, or calculating the average value of the curve segment, and the like. And then all the characteristic data are combined into a power generation data characteristic set and a power utilization data characteristic set.
Referring to S306 shown in fig. 3, the carbon emission on the power generation side and the carbon emission measured by electricity are respectively calculated according to the power generation data feature set and the electricity consumption data feature set, and are respectively multiplied by the respective influence factors to calculate the final carbon emission.
Specifically, the feature data sum is obtained by summing the feature data, and the feature data sum in a period of time comprises the power generation data feature data sum and the power utilization data feature data sum.
And respectively solving the carbon emission on the power generation side and the carbon emission on the power utilization side based on the characteristic data sum of the power generation data and the characteristic data sum of the power utilization data.
And respectively giving corresponding influence factors based on the carbon emission of the power generation side and the carbon emission of the power utilization side, and then calculating an average value to obtain final carbon emission data. Wherein the influencing factor is a correct rate of the detected value of the carbon emission.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples" or the like mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (10)
1. A city carbon emission detection system based on electric power big data is characterized by comprising:
the data acquisition module is used for acquiring power big data containing power generation data and power utilization data;
the data analysis module is used for respectively generating a power generation curve graph and a power utilization curve graph according to the electric power big data and the time corresponding to the electric power big data;
the image trimming module is used for cutting and reconstructing the overrun points in the power generation curve graph and the power utilization curve graph according to a preset rule to generate a power generation estimation graph and a power utilization estimation graph;
the image segmentation module is used for segmenting the power generation estimation graph and the power generation estimation graph into a plurality of power generation curve segments and power utilization curve segments according to the same curve modeling rule;
the characteristic extraction module is used for extracting characteristic data of each power generation curve segment and each power utilization curve segment and forming a power generation data characteristic set and a power utilization data characteristic set;
and the carbon emission measuring and calculating module is used for respectively calculating the carbon emission of the power generation side and the carbon emission of the electricity utilization measuring according to the power generation data characteristic set and the electricity utilization data characteristic set, and respectively multiplying the carbon emission and the carbon emission by respective influence factors to calculate the final carbon emission.
2. The electric power big data-based urban carbon emission detection system according to claim 1, wherein the preset rules comprise:
dividing curves on two sides of the overrun point into a common segment, a trimming segment and a reconstruction segment;
and after the reconstruction segment is replaced by the sine curve, the reconstruction segment is connected to the ordinary segment by modifying the curvature of the trimming segment.
3. The system for detecting urban carbon displacement based on electric power big data according to claim 1, wherein the curve modeling rule comprises:
judging the lifting amplitude of the curve and the length of the lifting time period;
respectively setting a time period threshold value and a lifting amplitude threshold value, and setting the corresponding curve segment as a power generation curve segment or a power consumption curve segment after the time period exceeds the time period threshold value and the lifting amplitude threshold value.
4. The urban carbon displacement detection system based on electric power big data according to claim 1, wherein the power generation data characteristic and the power consumption data characteristic are average power.
5. The urban carbon displacement detection system based on electric power big data according to claim 1, wherein the time period of the power generation estimation map and the power utilization estimation map is one day.
6. A city carbon emission detection method based on electric power big data is characterized by comprising the following steps:
acquiring power big data comprising power generation data and power utilization data;
respectively generating a power generation curve graph and a power utilization curve graph according to the electric power big data and the time corresponding to the electric power big data;
cutting and reconstructing the overrun points in the power generation curve graph and the power utilization curve graph according to a preset rule to generate a power generation estimation graph and a power utilization estimation graph;
dividing the power generation estimation diagram and the power generation estimation diagram into a plurality of power generation curve segments and power utilization curve segments according to the same curve modeling rule;
extracting the characteristic data of each power generation curve segment and each power utilization curve segment to form a power generation data characteristic set and a power utilization data characteristic set;
and respectively calculating the carbon emission of the power generation side and the carbon emission measured by electricity according to the power generation data characteristic set and the electricity utilization data characteristic set, and respectively multiplying the carbon emission and the carbon emission measured by the electricity utilization data characteristic set by respective influence factors to calculate the final carbon emission.
7. The method for detecting urban carbon emission based on electric power big data according to claim 6, wherein the preset rules comprise:
dividing the curves at two sides of the overrun point into a common section, a trimming section and a reconstruction section;
and after the reconstruction segment is replaced by the sine curve, the reconstruction segment is connected to the ordinary segment by modifying the curvature of the trimming segment.
8. The method for detecting urban carbon emission based on electric power big data according to claim 6, wherein the curve modeling rule comprises:
judging the lifting amplitude of the curve and the length of the lifting time period;
respectively setting a time period threshold value and a lifting amplitude threshold value, and setting the corresponding curve segment as a power generation curve segment or a power utilization curve segment after the time period exceeds the time period threshold value and the lifting amplitude threshold value.
9. The method for detecting urban carbon displacement based on electric power big data according to claim 6, wherein the power generation data characteristic and the power consumption data characteristic are average power.
10. The urban carbon emission detection method based on the power big data is characterized in that the time period of the power generation estimation map and the power utilization estimation map is one day.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210770205.2A CN115249089A (en) | 2022-06-30 | 2022-06-30 | Urban carbon emission detection system and method based on electric power big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210770205.2A CN115249089A (en) | 2022-06-30 | 2022-06-30 | Urban carbon emission detection system and method based on electric power big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115249089A true CN115249089A (en) | 2022-10-28 |
Family
ID=83700226
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210770205.2A Pending CN115249089A (en) | 2022-06-30 | 2022-06-30 | Urban carbon emission detection system and method based on electric power big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115249089A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116362138A (en) * | 2023-04-07 | 2023-06-30 | 广东海洋大学 | Artificial intelligence park carbon monitoring method based on big data |
-
2022
- 2022-06-30 CN CN202210770205.2A patent/CN115249089A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116362138A (en) * | 2023-04-07 | 2023-06-30 | 广东海洋大学 | Artificial intelligence park carbon monitoring method based on big data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106022259B (en) | A kind of mountain road extracting method based on laser point cloud three-dimensional feature descriptive model | |
| CN102955160A (en) | Three-dimensional laser radar technology based transmission line tower parameter determination method | |
| CN112132795B (en) | Electric tower disaster risk assessment method and system based on LiDAR point cloud | |
| CN111796298A (en) | Automatic point cloud point supplementing method for laser LiDAR power line | |
| CN115249089A (en) | Urban carbon emission detection system and method based on electric power big data | |
| CN114611834B (en) | Power station site selection evaluation planning method based on multidimensional feature analysis | |
| CN103516912A (en) | Mobile terminal locating and power saving method | |
| CN114117702A (en) | Point cloud-based automatic reverse modeling method for power transmission line | |
| CN114565313A (en) | Geological disaster hidden danger identification and analysis evaluation method and system | |
| CN113420458A (en) | Wind field data refining processing method and device, terminal and medium | |
| CN116645789A (en) | Land natural disaster early warning management and control system based on unmanned aerial vehicle survey and drawing | |
| CN113762645A (en) | Natural disaster forecasting method and device | |
| CN116362138A (en) | Artificial intelligence park carbon monitoring method based on big data | |
| CN109521476B (en) | Dyke resistivity tomography observation system | |
| CN116758757B (en) | Highway maintenance inspection method, medium and equipment | |
| CN104050376A (en) | Transmission line thunder leading width calculating method based on actually measured lightning stroke data | |
| CN116401879B (en) | Method for simulating downstream evolution of breaking tailing sand | |
| CN112364301A (en) | Slope length-based near-ground wind speed statistics downscaling correction method | |
| CN115358507B (en) | Production construction project disturbance map spot soil erosion risk identification and assessment method | |
| CN116520881A (en) | Unmanned plane continuous tower foundation three-dimensional inspection path planning method based on laser point cloud | |
| CN116452604A (en) | Complex substation scene segmentation method, device and storage medium | |
| CN113432549B (en) | Tidal trench three-dimensional parameter extraction method and system based on unmanned aerial vehicle photogrammetry | |
| CN114396892B (en) | Track curvature measuring method for track traffic curve | |
| Hua et al. | The research of artificial shoreline extraction based on airborne LIDAR data | |
| CN112241440B (en) | Three-dimensional green quantity estimation and management method based on LiDAR point cloud data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |