A power grid node electric carbon factor monitoring method and device, a terminal device and a storage medium

Abstract

The application discloses a kind of power grid node electric carbon factor monitoring method, device, terminal equipment and storage medium, belong to electric carbon monitoring field.Method includes obtaining the real-time electric quantity of the monitored power grid area, meteorological parameter and power grid node structure parameter, obtains sulfur fluoride compound wavelength signal based on difference absorption laser radar three-dimensional scanning;According to sulfur fluoride compound wavelength signal and power grid node structure parameter, concentration field reconstruction is carried out, and sulfur fluoride compound three-dimensional concentration matrix is obtained;According to sulfur fluoride compound three-dimensional concentration matrix and meteorological parameter, area integral calculation is carried out to obtain sulfur fluoride compound emission flux;According to sulfur fluoride compound emission flux, real-time electric quantity and power grid node structure parameter, calculation is carried out, and node electric carbon factor is obtained;According to node electric carbon factor, power grid node structure parameter and sulfur fluoride compound three-dimensional concentration matrix, correlation is carried out, and electric carbon factor dynamic thermal map is generated, by implementation of the application, it can solve the problem of low precision of existing electric carbon factor evaluation.

Description

Technical Field

[0001] This invention relates to the field of electrical carbon monitoring technology, and in particular to a method, device, terminal equipment, and storage medium for monitoring electrical carbon factors at power grid nodes. Background Technology

[0002] The electrical carbon factor at power grid nodes is a core indicator in power system carbon accounting, directly impacting carbon market trading, green electricity certification, and low-carbon dispatch optimization. Transformers, as key equipment at power grid nodes, primarily emit SF6 as greenhouse gas, and their global warming potential (GWP) of 23,500 is far higher than that of CO2. Therefore, accurate measurement of the electrical carbon factor at transformer nodes is a crucial foundation for comprehensively assessing power grid carbon emissions.

[0003] Currently, the measurement of the electrocarbon factor at power grid nodes (transformers) mainly employs two methods: statistical methods based on average emission factors and metrological methods based on carbon emission flow theory. The method based on average emission factors uses the regional power grid, such as the provincial average emission factor, and combines it with transformer losses to estimate carbon emissions and the node's electrocarbon factor. However, due to the lag in regional power grid emission factors, this method cannot reflect real-time carbon flow changes at the node level, and because it only considers CO2 emissions, the transformer's electrocarbon factor is severely underestimated. The method based on carbon emission flow theory is based on a full-link electrocarbon table or graph convolutional network model, using virtual carbon flow tracking or power grid topology data prediction to achieve node-level carbon accounting and electrocarbon factor calculation. Although the accuracy of node accounting has improved, problems such as missing SF6 monitoring and data update delays still exist. Therefore, how to improve the accuracy of electrocarbon factor assessment and reflect the actual emissions at power grid nodes has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a method, apparatus, terminal equipment, and storage medium for monitoring the carbon factor at power grid nodes, which can effectively solve the problem that the existing technology has low accuracy in assessing the carbon factor and cannot reflect the actual emissions at power grid nodes.

[0005] An embodiment of the present invention provides a method for monitoring the electrocarbon factor at power grid nodes, comprising: The system acquires real-time power consumption, meteorological parameters, and grid node structure parameters for the monitored power grid area, and performs three-dimensional scanning based on differential absorption lidar to obtain wavelength signals of sulfur and fluorine compounds. Based on the wavelength signal of the sulfur and fluorine compounds and the structural parameters of the power grid nodes, the concentration field is reconstructed to obtain a three-dimensional concentration matrix of sulfur and fluorine compounds. The emission flux of sulfur and fluorine compounds is calculated by performing regional integration based on the three-dimensional concentration matrix of sulfur and fluorine compounds and the meteorological parameters. The node electrocarbon factor is calculated based on the sulfur and fluorine compound emission flux, the real-time electricity consumption, and the grid node structural parameters. A dynamic thermogram of the electric carbon factor is generated by correlating the node's electric carbon factor, the grid node's structural parameters, and the three-dimensional concentration matrix of sulfur and fluorine compounds.

[0006] Further, based on the wavelength signal of the sulfur-fluorinated compounds and the structural parameters of the power grid nodes, a concentration field reconstruction is performed to obtain a three-dimensional concentration matrix of sulfur-fluorinated compounds, including: The wavelength signal of the sulfur-fluorine compound is filtered and denoised to obtain the processed wavelength signal of the sulfur-fluorine compound; and the intensity of the absorption wavelength signal is determined based on the processed wavelength signal of the sulfur-fluorine compound. Based on the absorption wavelength signal intensity, the preset absorption wavelength baseline calibration data, the reference wavelength signal intensity, and the reference wavelength baseline calibration data, the range-resolved concentration at each three-dimensional scanning angle of the differential absorption lidar is calculated; wherein, the reference wavelength signal intensity represents the echo signal intensity acquired by the differential absorption lidar at a reference wavelength that is not absorbed by sulfur and fluorine compounds; the reference wavelength baseline calibration data represents the preset signal baseline data of the differential absorption lidar in an environment without sulfur and fluorine compounds. Based on the monitoring area boundary and preset spatial resolution in the power grid node structure parameters, a three-dimensional grid system is constructed with the transformer to be monitored in the power grid area to be monitored as the center. Under the aforementioned three-dimensional mesh system, the projection matrix is ​​determined based on the intersection points of the laser beam path of the differential absorption lidar and the three-dimensional mesh system. Iterative algebraic reconstruction is performed based on the distance-resolved concentration, the projection matrix, and the current concentration matrix until the root mean square error of the concentration matrix difference between adjacent iterations converges, resulting in the final three-dimensional concentration matrix of sulfur and fluorine compounds; wherein, the current concentration matrix during the first algebraic reconstruction is the preset initial concentration matrix under the three-dimensional grid system.

[0007] Furthermore, the meteorological parameters include: wind direction, wind speed, and atmospheric stability parameters; Based on the three-dimensional concentration matrix of sulfur and fluorine compounds and the meteorological parameters, the emission flux of sulfur and fluorine compounds is calculated by regional integration, including: Based on the aforementioned three-dimensional mesh system, the wind direction is converted into a coordinate direction vector; The corresponding horizontal and vertical diffusion coefficients are determined based on the atmospheric stability parameters, and the preset Gaussian plume model is corrected based on the horizontal and vertical diffusion coefficients. In the three-dimensional space corresponding to the three-dimensional mesh system, the cross section perpendicular to the coordinate direction vector is used as the calculation cross section of the modified Gaussian plume model, and the angle between the coordinate direction vector and the normal direction of the calculation cross section is used as the calculation angle. The total regional emission flux is obtained by performing double integral calculations in the modified Gaussian plume model based on the calculated cross section, the calculated angle, and the wind speed. The total emission flux of several regions is calculated based on a preset interval, and the average value of the total emission flux of several regions is taken as the emission flux of sulfur and fluorine compounds.

[0008] Further, based on the sulfur and fluorine compound emission flux, the real-time electricity consumption, and the grid node structural parameters, the node electrocarbon factor is calculated, including: Based on the sulfur and fluorine compound emission flux and the preset collection period duration, the carbon equivalent corresponding to the sulfur and fluorine compound emission flux is calculated. Based on the power supply structure information in the power grid node structure parameters, carbon emissions from non-sulfur and fluorine compound sources are obtained; The total carbon emissions of the grid node to be monitored are calculated by adding the carbon emissions from non-sulfur and fluorine compound sources and the carbon equivalents corresponding to the sulfur and fluorine compound emission fluxes. The ratio of total carbon emissions to real-time electricity consumption is used as the node carbon factor.

[0009] Further, based on the correlation between the node electrocarbon factor, the grid node structural parameters, and the three-dimensional concentration matrix of sulfur and fluorine compounds, a dynamic thermogram of the electrocarbon factor is generated, including: Based on the power grid node geographical coordinate information in the power grid node structural parameters, the node electrocarbon factor, the three-dimensional concentration matrix of sulfur and fluorine compounds, and the power grid node geographical coordinate information are spatially correlated to construct a mapping relationship; The values ​​corresponding to the node carbon factor are divided into preset hierarchical intervals, and the geographic coordinate information of the power grid node corresponding to the node carbon factor in different hierarchical intervals is labeled with the same color gradient based on the mapping relationship; wherein, different hierarchical intervals correspond to different color gradients. Extract data from different concentration regions in the three-dimensional concentration matrix of sulfur and fluorine compounds, and label the concentration regions corresponding to the data of different concentration regions with the same color gradient based on the mapping relationship and the geographical coordinate information of the power grid nodes. The node carbon factor is labeled, and the labeled grid node geographic coordinates are labeled in the corresponding labeled concentration area to generate a dynamic heat map of the carbon factor.

[0010] Furthermore, it also includes: The leakage intensity is calculated based on the ratio of the sulfur and fluorine compound emission flux to the area of ​​the power grid region to be monitored; The leakage intensity is compared with a preset intensity threshold. If the leakage intensity is less than or equal to the intensity threshold, maintain the current scanning frequency of the differential absorption lidar; If the leakage intensity is greater than the intensity threshold, the scanning frequency of the differential absorption lidar is updated to a preset first scanning frequency until the leakage intensity is less than or equal to the intensity threshold within a preset time period.

[0011] Furthermore, the real-time electricity consumption is obtained through a smart meter; the meteorological parameters are obtained through a weather station. The timestamps of the data collected by the smart meters, weather stations, and differential absorption lidar are consistent.

[0012] As an improvement to the above solution, another embodiment of the present invention provides a power grid node carbon factor monitoring device, comprising: The data acquisition module is used to acquire real-time power consumption, meteorological parameters, and grid node structure parameters of the power grid area to be monitored, and to acquire the wavelength signals of sulfur and fluorine compounds by performing three-dimensional scanning based on differential absorption lidar. The concentration matrix construction module is used to reconstruct the concentration field based on the wavelength signal of the sulfur-fluorine compound and the structural parameters of the power grid node to obtain a three-dimensional concentration matrix of the sulfur-fluorine compound. The emission flux calculation module is used to calculate the emission flux of sulfur and fluorine compounds by performing regional integration based on the three-dimensional concentration matrix of sulfur and fluorine compounds and the meteorological parameters. The node carbon factor calculation module is used to calculate the node carbon factor based on the sulfur and fluorine compound emission flux, the real-time electricity, and the grid node structure parameters. The node electrocarbon factor visualization module is used to generate a dynamic thermogram of the electrocarbon factor by correlating the node electrocarbon factor, the grid node structural parameters, and the three-dimensional concentration matrix of sulfur and fluorine compounds.

[0013] Another embodiment of the present invention provides a terminal device, 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, it implements a method for monitoring the carbon factor of a power grid node as described in the above embodiments.

[0014] Another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform a power grid node carbon factor monitoring method as described in the above embodiment.

[0015] By implementing this invention, at least the following beneficial effects are achieved: This invention provides a method, device, terminal equipment, and storage medium for monitoring the electrocarbon factor at power grid nodes. The method utilizes differential absorption lidar three-dimensional scanning to acquire wavelength signals of sulfur and fluorine compounds (SFCCs), accurately capturing SFCC information at nodes. Through concentration field reconstruction and regional integration, the emission flux of SFCCs is quantified and ultimately added to the total carbon emissions at the node. This fundamentally fills the gap in SFCC monitoring by incorporating SFCC emissions into the calculation of the electrocarbon factor at power grid nodes, overcoming the shortcomings of existing technologies that only focus on CO2. This makes the calculation results more consistent with the actual emissions at the nodes, preventing the electrocarbon factor from being underestimated. Simultaneously, real-time electricity and meteorological parameters are collected and rapidly processed through concentration field reconstruction and regional integration to directly calculate the node electrocarbon factor and generate a dynamic heat map. This eliminates the need to rely on historical statistical data or complex forecasts, enabling real-time updates of the electrocarbon factor and accurately capturing changes in carbon flow at power grid nodes. Based on the structural parameters of power grid nodes, a three-dimensional concentration matrix of sulfur and fluorine compounds is obtained by reconstructing the concentration field using the wavelength signals of sulfur and fluorine compounds and the structural parameters of the power grid nodes. This achieves precise spatial coverage at the node level, rather than regional averaging calculation, ensuring that the electrocarbon factor can truly reflect the actual emission level of the node, thus overcoming the accuracy limitations of existing methods. Therefore, this invention improves the assessment accuracy of the electrocarbon factor and also reflects the actual emission situation of power grid nodes. Attached Figure Description

[0016] Figure 1 This is a schematic flowchart of a method for monitoring the carbon factor of a power grid node according to an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the principle of a method for monitoring the carbon factor of a power grid node according to an embodiment of the present invention; Figure 3 This is another schematic diagram of a process provided in one embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of a power grid node carbon factor monitoring device provided in an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] See Figure 1To address the problem that existing technologies have low accuracy in assessing electrical carbon factors and cannot reflect the actual emissions at power grid nodes, an embodiment of the present invention provides a flowchart of a method for monitoring electrical carbon factors at power grid nodes, including: S1. Obtain real-time power, meteorological parameters and grid node structure parameters of the power grid area to be monitored, and perform three-dimensional scanning based on differential absorption lidar to obtain the wavelength signal of sulfur and fluorine compounds. Specifically, real-time electricity consumption represents the cumulative electrical energy consumed or generated within the monitored power grid area during a set data collection period, measured in units of [unit missing]. This is the fundamental measurement data for calculating the electrocarbon factor. Meteorological parameters represent atmospheric environmental parameters affecting the diffusion of sulfur-fluorinated compounds (SF6, hereinafter collectively referred to as sulfur-fluorinated compounds), including wind direction, wind speed, and atmospheric stability parameters. Grid node structure parameters represent structural information related to the grid node to be monitored, including the node's power supply structure (the proportion of thermal power, purchased power, etc.), monitoring area boundaries, grid node geographical coordinates, and the location of the transformer to be monitored. Differential absorption lidar (DIAL) is a device based on the principle of differential laser absorption that enables three-dimensional spatial scanning and detection of gas concentration. Its core function is to capture the characteristic wavelength signals of sulfur-fluorinated compounds. The sulfur-fluorinated compound wavelength signal is the echo signal received by the differential absorption lidar after sulfur-fluorinated compounds absorb laser light of a specific wavelength. Its intensity is related to the concentration of sulfur-fluorinated compounds and is the core signal source for monitoring sulfur-fluorinated compounds.

[0019] Preferably, the real-time electricity consumption is obtained through a smart meter; the meteorological parameters are obtained through a weather station. The timestamps of the data collected by the smart meters, weather stations, and differential absorption lidar are consistent.

[0020] To illustrate, real-time electricity consumption is collected through smart meters, meteorological parameters such as wind direction, wind speed, and atmospheric stability are collected through weather stations, and the azimuth of power grid nodes (centered on transformers) is determined using differential absorption lidar. 0 -360 0 Pitch angle -10 0 -90 0 The system employs a spiral 3D scanning method to acquire wavelength signals of sulfur and fluorocarbons (SFFCs). Simultaneously, it achieves time synchronization between smart meters, weather stations, and differential absorption lidar, ensuring spatiotemporal data matching. It collects the fundamental data required for calculating the carbon factor, where the SFFC wavelength signal is crucial for capturing SFFC emissions, real-time electricity consumption serves as the measurement benchmark for carbon factor calculation, meteorological parameters influence SFFC diffusion patterns, and grid node structural parameters provide boundary conditions for subsequent spatial positioning and calculation.

[0021] S2. Based on the wavelength signal of the sulfur-fluorine compound and the structural parameters of the power grid node, the concentration field is reconstructed to obtain the three-dimensional concentration matrix of the sulfur-fluorine compound. Specifically, concentration field reconstruction refers to the process of converting discrete wavelength signals into a representation of the concentration distribution of sulfur and fluorine compounds in three-dimensional space through signal processing, spatial grid partitioning, and iterative algorithms. The three-dimensional concentration matrix of sulfur and fluorine compounds stores the concentration data of sulfur and fluorine compounds at various points in the three-dimensional monitoring space in matrix form, with each element corresponding to the concentration value of sulfur and fluorine compounds at specific spatial coordinates.

[0022] Preferably, a three-dimensional concentration matrix of sulfur and fluorine compounds is obtained by reconstructing the concentration field based on the wavelength signal of the sulfur and fluorine compounds and the structural parameters of the power grid nodes, including: The wavelength signal of the sulfur-fluorine compound is filtered and denoised to obtain the processed wavelength signal of the sulfur-fluorine compound; and the intensity of the absorption wavelength signal is determined based on the processed wavelength signal of the sulfur-fluorine compound. Based on the absorption wavelength signal intensity, the preset absorption wavelength baseline calibration data, the reference wavelength signal intensity, and the reference wavelength baseline calibration data, the range-resolved concentration at each three-dimensional scanning angle of the differential absorption lidar is calculated; wherein, the reference wavelength signal intensity represents the echo signal intensity acquired by the differential absorption lidar at a reference wavelength that is not absorbed by sulfur and fluorine compounds; the reference wavelength baseline calibration data represents the preset signal baseline data of the differential absorption lidar in an environment without sulfur and fluorine compounds. Based on the monitoring area boundary and preset spatial resolution in the power grid node structure parameters, a three-dimensional grid system is constructed with the transformer to be monitored in the power grid area to be monitored as the center. Under the aforementioned three-dimensional mesh system, the projection matrix is ​​determined based on the intersection points of the laser beam path of the differential absorption lidar and the three-dimensional mesh system. Iterative algebraic reconstruction is performed based on the distance-resolved concentration, the projection matrix, and the current concentration matrix until the root mean square error of the concentration matrix difference between adjacent iterations converges, resulting in the final three-dimensional concentration matrix of sulfur and fluorine compounds; wherein, the current concentration matrix during the first algebraic reconstruction is the preset initial concentration matrix under the three-dimensional grid system.

[0023] Specifically, filtering and denoising refers to the process of removing interference from the original sulfur and fluorine compound wavelength signal, with the aim of improving signal purity. The absorption wavelength signal intensity S on This represents the echo signal intensity acquired by the differential absorption lidar at the characteristic absorption wavelength of sulfur and fluorine compounds, and is positively correlated with the concentration of sulfur and fluorine compounds. Reference wavelength signal intensity S off This represents the echo signal intensity acquired by the differential absorption lidar at a reference wavelength that is not significantly absorbed by sulfur and fluorine compounds, used to eliminate system background interference. Absorption wavelength baseline calibration data B onThis indicates that during system startup, in a sulfur-free and fluorine-free environment, the lidar acquires a signal baseline for the absorption wavelength, used to correct system errors in the absorption wavelength signal intensity. Reference wavelength baseline calibration data B. off The system startup parameters represent the baseline signal acquired by the lidar at a reference wavelength in a sulfur- and fluorine-free environment, used to correct systematic errors in the reference wavelength signal intensity. Distance-resolved concentration represents the sulfur- and fluorine compound concentration at different distances under each scanning angle, serving as the foundational discrete data for constructing the three-dimensional concentration matrix. The three-dimensional mesh system represents a set of three-dimensional voxel grids centered on the transformer to be monitored, divided according to a preset spatial resolution. Each voxel corresponds to a unique spatial coordinate, used to locate the spatial position of the concentration data. The projection matrix A reflects the intersection between the lidar scanning path and the three-dimensional mesh system, with dimensions equal to the number of scans multiplied by the number of voxels; it is the core parameter for iterative reconstruction. Iterative algebraic reconstruction employs Algebraic Reconstruction Technology (ART). The root mean square error (RMSE) measures the difference between the concentration matrices of two adjacent iterations, used to determine whether the reconstruction has converged. The initial concentration matrix is ​​constructed based on the industry-standard preset background concentration of sulfur- and fluorine compounds during the first iteration, serving as the starting data for iterative reconstruction.

[0024] Schematic, the sulfur-fluorinated compound wavelength signal is first filtered and denoised to obtain a processed sulfur-fluorinated compound wavelength signal; the absorption wavelength signal intensity is then determined based on the processed sulfur-fluorinated compound wavelength signal; next, the range-resolved concentration at each three-dimensional scanning angle of the differential absorption lidar is calculated based on the absorption wavelength signal intensity, preset absorption wavelength baseline calibration data, reference wavelength signal intensity, and reference wavelength baseline calibration data; wherein, the reference wavelength signal intensity characterizes the echo signal intensity acquired by the differential absorption lidar at a reference wavelength not absorbed by sulfur-fluorinated compounds; the reference wavelength baseline calibration data characterizes the preset absorption wavelength baseline calibration data of the differential absorption lidar in a sulfur-free environment. The signal baseline data is obtained; then, based on the monitoring area boundary in the power grid node structure parameters and the preset spatial resolution, a three-dimensional grid system is constructed with the transformer to be monitored within the power grid area as the center; under the three-dimensional grid system, the projection matrix is ​​determined according to the intersection of the laser beam path of the differential absorption lidar and the three-dimensional grid system; finally, iterative algebraic reconstruction is performed based on the distance-resolved concentration, the projection matrix, and the current concentration matrix until the root mean square error of the concentration matrix difference between adjacent iterations converges, to obtain the final three-dimensional concentration matrix of sulfur and fluorine compounds; wherein, the current concentration matrix when performing the first algebraic reconstruction is the preset initial concentration matrix under the three-dimensional grid system.

[0025] In a preferred embodiment of the present invention, a 5-point moving average algorithm is used to filter and denoise the wavelength signal of sulfur-fluorine compounds, and abnormal peaks caused by electromagnetic interference are eliminated using the 3σ criterion. The signal intensity corresponding to the absorption wavelength is extracted from the processed signal, which is the absorption wavelength signal intensity. By eliminating interference components in the signal, the accuracy of the absorption wavelength signal intensity is ensured. Based on the formula... (α is the differential absorption coefficient) Calculate the distance-resolved concentration C1 at each three-dimensional scanning angle. Based on the monitoring area boundary in the power grid node structural parameters, delineate a three-dimensional monitoring area centered on the transformer to be monitored, with x∈[-20m,20m], y∈[-20m,20m], and z∈[0m,10m], and set at 0.1m... 3 The preset spatial resolution divides the space into a uniform voxel grid, forming a total of 16,000,000 voxels, each assigned a unique spatial coordinate (x, y). i y j , z k In a three-dimensional mesh system, the laser beam scanning path of a differential absorption lidar is simulated, and the intersection points of each laser beam with the voxel mesh are calculated. Based on the intersection point relationships, a projection matrix A is constructed. Starting from a preset initial concentration matrix C0, and combining the range-resolved concentration and the projection matrix, the following formula is used... (λ is a relaxation factor of 0.05-0.1) Iterate and update until the root mean square error (RMSE) of the concentration matrix difference between two adjacent iterations is less than 0.01 ppm, and then stop the iteration and output the final three-dimensional concentration matrix of sulfur and fluorine compounds.

[0026] This embodiment employs a 5-point moving average and a 3σ criterion to effectively improve signal purity and reduce the impact of interference on concentration calculation. 0.1m 3 The high spatial resolution enables precise concentration localization at the node level, addressing the insufficient spatiotemporal resolution of traditional methods. Iterative reconstruction using the ART algorithm ensures the accuracy and convergence of the concentration matrix, providing reliable data support for emission flux calculations.

[0027] S3. Based on the three-dimensional concentration matrix of sulfur and fluorine compounds and the meteorological parameters, perform regional integration to calculate the emission flux of sulfur and fluorine compounds; Specifically, sulfur and fluorine compound emission flux represents the total amount of sulfur and fluorine compounds emitted in the monitored area per unit time, expressed in kg / h, and is a key parameter for calculating the carbon equivalent of sulfur and fluorine compounds.

[0028] Preferably, the meteorological parameters include: wind direction, wind speed, and atmospheric stability parameters; Based on the three-dimensional concentration matrix of sulfur and fluorine compounds and the meteorological parameters, the emission flux of sulfur and fluorine compounds is calculated by regional integration, including: Based on the aforementioned three-dimensional mesh system, the wind direction is converted into a coordinate direction vector; The corresponding horizontal and vertical diffusion coefficients are determined based on the atmospheric stability parameters, and the preset Gaussian plume model is corrected based on the horizontal and vertical diffusion coefficients. In the three-dimensional space corresponding to the three-dimensional mesh system, the cross section perpendicular to the coordinate direction vector is used as the calculation cross section of the modified Gaussian plume model, and the angle between the coordinate direction vector and the normal direction of the calculation cross section is used as the calculation angle. The total regional emission flux is obtained by performing double integral calculations in the modified Gaussian plume model based on the calculated cross section, the calculated angle, and the wind speed. The total emission flux of several regions is calculated based on a preset interval, and the average value of the total emission flux of several regions is taken as the emission flux of sulfur and fluorine compounds.

[0029] Specifically, wind direction refers to the direction of atmospheric airflow, represented by the angle θ with the x-axis, which affects the diffusion direction of sulfur and fluorine compounds. Wind speed v is the velocity of atmospheric airflow, in m / s, which affects the diffusion rate of sulfur and fluorine compounds. Atmospheric stability parameters characterize the diffusion capacity of atmospheric turbulence, using the Pasquill stability level (default level B) calculated based on the temperature gradient. The coordinate direction vector is a vector that converts the wind direction to a spatial coordinate system consistent with the three-dimensional grid system, in the form (ucosθ, vsinθ, 0), used to unify the spatial calculation benchmark. u represents the real-time scalar wind speed collected by the meteorological monitoring station, in m / s. Horizontal diffusion coefficient. This represents the diffusion coefficient of sulfur-fluorine compounds in the crosswind direction (perpendicular to the diffusion direction), expressed in meters (m), and is related to atmospheric stability and downwind distance. Vertical diffusion coefficient. The vertical diffusion coefficient of sulfur and fluorine compounds, expressed in meters (m), is related to atmospheric stability and downwind distance. The Gaussian plume model is a classic model describing the diffusion of pollutants in the atmosphere, assuming a normal distribution of pollutants in both the crosswind and vertical directions, and is used to calculate pollutant emission fluxes. The computational cross section represents the section defined perpendicular to the diffusion direction (coordinate direction vector) of sulfur and fluorine compounds, used for integration calculations, covering the entire high-concentration area. The computational angle α is the angle between the coordinate direction vector and the normal direction of the computational cross section, used to correct directional errors in flux calculations. The preset interval is the time interval for continuously calculating the total emission flux of the region, with a default of 10 seconds, used to verify data stability.

[0030] Schematic, the wind direction is first converted into a coordinate direction vector based on the three-dimensional grid system; then, the corresponding horizontal and vertical diffusion coefficients are determined according to the atmospheric stability parameters, and the preset Gaussian plume model is corrected based on the horizontal and vertical diffusion coefficients; in the three-dimensional space corresponding to the three-dimensional grid system, the cross section perpendicular to the coordinate direction vector is used as the calculation cross section of the corrected Gaussian plume model, and the angle between the coordinate direction vector and the normal direction of the calculation cross section is used as the calculation angle; then, double integral calculation is performed in the corrected Gaussian plume model based on the calculation cross section, the calculation angle, and the wind speed to obtain the regional total emission flux; finally, several regional total emission fluxes are calculated according to preset intervals, and the average value of the several regional total emission fluxes is used as the sulfur and fluorine compound emission flux.

[0031] In a preferred embodiment of the present invention, based on the spatial coordinate system of a three-dimensional grid system, the wind direction θ in the meteorological parameters is converted into a coordinate direction vector (ucosθ, vsinθ, 0), ensuring that the wind direction data is consistent with the spatial reference of the three-dimensional concentration field. The horizontal diffusion coefficient is determined according to atmospheric stability parameters (e.g., Level B). =0.8x 0.85 (x is the downwind distance), vertical diffusion coefficient =0.5x 0.75 Simultaneously, the average height z0 = 3m of the high-incidence areas of transformer sulfur and fluorine compound leakage (bushings, valves) can be used as the emission source height to correct the vertical coordinate reference of the Gaussian plume model. In the three-dimensional space corresponding to the three-dimensional mesh system, a calculation section S with an area of ​​40m × 10m = 400m² is defined perpendicular to the coordinate direction vector. This section is divided into 40,000 micro-element areas dS of 0.1m × 0.1m. The angle α between the calculation coordinate direction vector and the normal direction of the calculation section is used as the calculation angle. The concentration C(x, y, z) corresponding to each micro-element area dS is obtained by interpolation from the three-dimensional concentration matrix. The micro-element flux is calculated according to the Gaussian plume flux formula. ( (where is the angle between the wind speed direction and the direction of the normal to the infinitesimal element). Perform a double integral over the entire computational cross-section: The trapezoidal integral method was used to solve for the total regional emission flux (unit: kg / h, converted to 6.16 kg / m³ using a concentration-mass conversion factor of 1 ppm). 3 (Conversion). Calculate three times consecutively (10-second intervals between each calculation), remove outliers that deviate from the mean by more than two standard deviations, and take the arithmetic mean as the final emission flux to ensure data stability.

[0032] This embodiment customizes the diffusion coefficient based on atmospheric stability and combines it with model correction to improve the scenario adaptability of flux calculation. The combination of infinitesimal element partitioning and double integrals ensures the accuracy of flux calculation and avoids errors caused by regional average estimation.

[0033] S4. Calculate the node electrocarbon factor based on the sulfur and fluorine compound emission flux, the real-time electricity, and the grid node structure parameters; Specifically, the nodal carbon factor represents the carbon emissions per unit of electricity generated at the monitored power grid node, in units of... It is a core indicator for measuring the carbon emission intensity of a node.

[0034] Preferably, the node electrocarbon factor is calculated based on the sulfur and fluorine compound emission flux, the real-time electricity consumption, and the grid node structural parameters, including: Based on the sulfur and fluorine compound emission flux and the preset collection period duration, the carbon equivalent corresponding to the sulfur and fluorine compound emission flux is calculated. Based on the power supply structure information in the power grid node structure parameters, carbon emissions from non-sulfur and fluorine compound sources are obtained; The total carbon emissions of the grid node to be monitored are calculated by adding the carbon emissions from non-sulfur and fluorine compound sources and the carbon equivalents corresponding to the emission fluxes of sulfur and fluorine compounds. The ratio of total carbon emissions to real-time electricity consumption is used as the node carbon factor.

[0035] Specifically, the data collection period Δt represents the data collection time cycle for real-time electricity consumption and sulfur-fluorinated compound (SFCC) emission flux, measured in hours (h), and is assumed to be consistent with the real-time electricity consumption data collection granularity. Carbon equivalent is the numerical value used to convert total SFCC emissions into equivalent CO2 emissions, serving as a unified benchmark for carbon emission accounting. Power supply structure information refers to the power source composition ratio of grid nodes, such as the proportion of thermal power, hydropower, wind power, photovoltaic power, and purchased electricity, which is the basis for calculating carbon emissions from non-SFCC sources. Carbon emissions from non-SFCC sources Q0 represent the CO2 emissions from other carbon emission sources besides SFCC (such as thermal power generation and carbon emissions implicit in purchased electricity), obtained using the time-of-use and zone-based carbon factor calculation method. Total carbon emissions Q represent the total carbon emissions of grid nodes during the data collection period, equal to the sum of SFCC carbon equivalent and non-SFCC carbon emissions.

[0036] Schematic, firstly, the carbon equivalent corresponding to the sulfur-fluorinated compound emission flux is calculated based on the sulfur-fluorinated compound emission flux and the preset collection period duration; then, carbon emissions from non-sulfur-fluorinated compound sources are obtained based on the power supply structure information in the power grid node structure parameters; next, the total carbon emissions of the power grid node to be monitored are calculated by adding the carbon emissions from non-sulfur-fluorinated compound sources and the carbon equivalent corresponding to the sulfur-fluorinated compound emission flux; finally, the ratio of the total carbon emissions to the real-time electricity consumption is used as the node's electrical carbon factor.

[0037] In a preferred embodiment of the present invention, based on the global warming potential (GWP) of sulfur fluoride compounds (GWP) of 23900 as specified in the IPCC standard, according to formula Q... SF6 =Φ×Δt×23900 to calculate the carbon equivalent of sulfur and fluorine compound emissions during the data collection period, where Φ is the sulfur and fluorine compound emission flux and Δt is the duration of the data collection period. Based on the power supply structure information in the power grid node structure parameters (e.g., 50% thermal power, 30% wind power, and 20% purchased electricity), the carbon emissions corresponding to each power source are obtained according to the time-of-use and zone-based carbon factor calculation method, and the carbon emissions from non-sulfur and fluorine compound sources are summed to obtain Q0. The carbon equivalent Q0 corresponding to sulfur and fluorine compounds is then calculated. SF6 Adding the carbon emissions Q0 from non-sulfur and fluorine compound sources, we get the total carbon emissions Q of the monitored grid node: Q = Q0 + Q SF6 Confirm that the time granularity of the real-time power W acquisition is consistent with the acquisition period duration Δt. Calculate the nodal carbon factor using the formula f=Q / W, and retain the result to 4 decimal places. The unit is _____. .

[0038] This embodiment covers all carbon emission sources, solving the problem of incomplete accounting caused by traditional methods that only consider CO2.

[0039] S5. Based on the correlation between the node electrocarbon factor, the power grid node structural parameters, and the three-dimensional concentration matrix of sulfur and fluorine compounds, a dynamic thermogram of the electrocarbon factor is generated.

[0040] Specifically, the dynamic heat map of the electrocarbon factor associates the three-dimensional concentration distribution of the nodal electrocarbon factor and sulfur and fluorine compounds with geographic coordinates, and uses color gradients to intuitively display the spatial distribution and temporal changes of carbon emission intensity and sulfur and fluorine compound concentrations in a visual chart.

[0041] Preferably, a dynamic thermogram of the electrical carbon factor is generated by correlating the node electrical carbon factor, the grid node structural parameters, and the three-dimensional concentration matrix of sulfur and fluorine compounds, including: Based on the power grid node geographical coordinate information in the power grid node structural parameters, the node electrocarbon factor, the three-dimensional concentration matrix of sulfur and fluorine compounds, and the power grid node geographical coordinate information are spatially correlated to construct a mapping relationship; The values ​​corresponding to the node carbon factor are divided into preset hierarchical intervals, and the geographic coordinate information of the power grid node corresponding to the node carbon factor in different hierarchical intervals is labeled with the same color gradient based on the mapping relationship; wherein, different hierarchical intervals correspond to different color gradients. Extract data from different concentration regions in the three-dimensional concentration matrix of sulfur and fluorine compounds, and label the concentration regions corresponding to the data of different concentration regions with the same color gradient based on the mapping relationship and the geographical coordinate information of the power grid nodes. The node carbon factor is labeled, and the labeled grid node geographic coordinates are labeled in the corresponding labeled concentration area to generate a dynamic heat map of the carbon factor.

[0042] Specifically, the geographic coordinate information of the power grid nodes refers to the spatial positioning information such as latitude, longitude, and altitude of the power grid nodes to be monitored (e.g., transformers, substations), used to associate concentration data and carbon factor data with actual geographical locations. The mapping relationship is a three-dimensional coupled mapping of concentration-carbon factor-geographical location, connecting the concentration of sulfur and fluorine compounds, the node electrochemical carbon factor, and geographic coordinates. The preset hierarchical intervals are several level intervals divided according to the numerical range of the node electrochemical carbon factor, used to achieve gradient visualization of the carbon factor. Color gradient annotation indicates that different saturations or hues of color correspond to different numerical intervals of electrochemical carbon factor or sulfur and fluorine compound concentrations, achieving intuitive data visualization.

[0043] Schematic, firstly, based on the geographical coordinates of the power grid nodes in the power grid node structural parameters, the node electrocarbon factor, the three-dimensional concentration matrix of sulfur and fluorine compounds, and the geographical coordinates of the power grid nodes are spatially correlated to construct a mapping relationship; then, the values ​​corresponding to the node electrocarbon factor are partitioned according to preset hierarchical intervals, and the geographical coordinates of the power grid nodes corresponding to the node electrocarbon factor in different hierarchical intervals are labeled with the same color gradient based on the mapping relationship; wherein, different hierarchical intervals correspond to different color gradients; next, data of different concentration regions in the three-dimensional concentration matrix of sulfur and fluorine compounds are extracted, and the concentration regions corresponding to different concentration regions are labeled with the same color gradient based on the mapping relationship and the geographical coordinates of the power grid nodes; finally, the labeled node electrocarbon factor and the labeled geographical coordinates of the power grid nodes are labeled in the corresponding labeled concentration regions to generate a dynamic heat map of the electrocarbon factor.

[0044] In a preferred embodiment of the present invention, the geographical coordinate information of the power grid nodes is first extracted from the structural parameters of the power grid nodes. The concentration values ​​of each point in the three-dimensional concentration matrix of the node's electrocarbon factor and sulfur-fluorine compounds are then bound to their corresponding geographical coordinates to construct a mapping relationship between concentration, carbon factor, and geographical location. The node's electrocarbon factor values ​​are then categorized into preset hierarchical ranges, such as 0-0.1. 0.1-0.2 0.2-0.3 The system is partitioned, and based on mapping relationships, the geographic coordinates of the electrocarbon factor corresponding to different grading intervals are labeled with the same color gradient, with higher numerical values ​​indicating higher color saturation. Then, data from different concentration regions in the 3D concentration matrix of sulfur and fluorine compounds are extracted. Based on mapping relationships and the geographic coordinates of power grid nodes, different concentration regions are labeled with the same color gradient, using the same color scheme as the corresponding electrocarbon factor grading intervals. Finally, the labeled electrocarbon factor, geographic coordinates, and labeled sulfur and fluorine compound concentration regions are overlaid to generate a dynamic heatmap of the electrocarbon factor containing spatial distribution and numerical gradients, supporting dynamic updates according to the electrocarbon factor update cycle.

[0045] The color gradient labeling and color-based linkage in this embodiment intuitively display the correlation between carbon factors and sulfur and fluorine compound concentrations, facilitating quick location of problem areas; it transforms abstract data into intuitive charts, enabling maintenance personnel to quickly grasp the carbon emission status of nodes.

[0046] Indicatively, it also includes: The leakage intensity is calculated based on the ratio of the sulfur and fluorine compound emission flux to the area of ​​the power grid region to be monitored; The leakage intensity is compared with a preset intensity threshold. If the leakage intensity is less than or equal to the intensity threshold, maintain the current scanning frequency of the differential absorption lidar; If the leakage intensity is greater than the intensity threshold, the scanning frequency of the differential absorption lidar is updated to a preset first scanning frequency until the leakage intensity is less than or equal to the intensity threshold within a preset time period.

[0047] Specifically, leakage intensity I is the sulfur and fluorine compound emission flux per unit area, with units of... This is used to quantify the severity of a leak. The intensity threshold is a critical value for judging the severity of a leak, including a first threshold and a second threshold, set based on industry safety standards and monitoring requirements. The first threshold is the dividing line between low-intensity and medium-intensity leaks, with a default value of 0.001. The second threshold is the dividing line between medium-intensity and high-intensity leakage, with a default value of 0.005. The current scanning frequency is the time interval for the differential absorption lidar to perform a 3D scan, with a default of 15 minutes per scan, which can be dynamically adjusted according to the leakage intensity. The first scanning frequency is the updated scanning frequency, with a shorter time interval compared to the current scanning frequency.

[0048] Schematic, the leakage intensity is first calculated based on the ratio of the sulfur and fluorine compound emission flux to the area of ​​the power grid to be monitored; then the leakage intensity is compared with a preset intensity threshold; if the leakage intensity is less than or equal to the intensity threshold, the current scanning frequency of the differential absorption lidar is maintained; if the leakage intensity is greater than the intensity threshold, the scanning frequency of the differential absorption lidar is updated to a preset first scanning frequency until the leakage intensity is less than or equal to the intensity threshold within a preset time period.

[0049] In a preferred embodiment of the present invention, according to the formula I=Φ / S 面 Calculate the leakage intensity, where Φ is the sulfur and fluorine compound emission flux, and S 面 Area of ​​the power grid to be monitored (unit: m) 2 The calculated leakage intensity I is compared with a preset first threshold (0.001). ) and the second threshold (0.005) The leakage intensity I is compared to determine the leakage level. (The following is a separate, unrelated sentence: "When the leakage intensity I ≤ 0.001") Under these conditions, maintain the default scanning frequency of the differential absorption lidar (15 minutes / scan). With a leakage intensity of 0.001... <I≤0.005 In this case, the scanning frequency of the differential absorption lidar is increased to 5 minutes / time. When the leakage intensity I > 0.005... In the event of an abnormal leakage intensity, the scanning frequency will be increased to 1 minute / time until the leakage intensity is less than or equal to the intensity threshold within a preset time period, at which point the scanning frequency will be restored to 15 minutes / time. Simultaneously, an alarm message containing the leakage location, leakage intensity, and abnormal electrocarbon factor value will be generated and pushed to the operation and maintenance platform.

[0050] This embodiment quantifies leakage intensity into graded responses, providing a precise basis for monitoring and avoiding over- or under-monitoring. Dynamically adjusting the scanning frequency optimizes resource consumption and reduces monitoring costs while ensuring monitoring effectiveness. Timely alarms for high-intensity leaks improve the timeliness of leak handling and reduce environmental and safety risks.

[0051] In a preferred embodiment of the present invention, such as Figure 2As shown, the data acquisition layer is the data input source, corresponding to multi-source data acquisition and time synchronization of acquisition devices. It acquires sulfur and fluorine compound wavelength signals based on differential absorption lidar (DIAL), real-time electricity consumption based on smart meters, meteorological parameters based on meteorological monitoring stations, and performs time synchronization based on an edge computing gateway. The data processing layer corresponds to sulfur and fluorine compound concentration field reconstruction, emission flux calculation, and node electrocarbon factor calculation, outputting the node electrocarbon factor. Finally, the application layer realizes node electrocarbon factor visualization and SF6 leakage early warning, and generates an SF6 emission report. For example, in the monitoring area of ​​a 110kV substation's GIS equipment, the differential absorption lidar system detected SF6 emission fluxes of 1.25 kg / h, 1.32 kg / h, and 1.28 kg / h three times consecutively (each time at 15-minute intervals), all exceeding the 1.2 kg / h alarm threshold. The system immediately triggered an audible and visual alarm and pushed alarm information to the operation and maintenance platform via a remote communication module, simultaneously activating the on-site ventilation device.

[0052] In a preferred embodiment of the present invention, such as Figure 3 As shown, the Differential Absorption LiDAR (DIAL) automatically performs baseline calibration, sets wavelength scanning parameters, and establishes a three-dimensional spatial scanning path centered on the transformer. Subsequently, DIAL performs fully automated scanning measurements according to a set time cycle, transmitting the scanned spectral data to the edge computing gateway in real time for processing. After data processing, the data is uploaded to the main station system via an encrypted channel, generating a geocoded SF6 concentration distribution map, emission source location map, and monitoring report. When an abnormal increase in SF6 emission flux is detected, a leakage early warning mechanism is triggered, dynamically adjusting the DIAL scanning time interval based on the leakage intensity. The system adopts containerized deployment, supporting remote parameter configuration and algorithm upgrades. Through modular design, a single system can simultaneously monitor 3-5 adjacent transformers, significantly reducing costs. For example, the monitoring area S of a 220kV substation... 面 =50m 2 The lidar detected an SF6 emission flux of Φ = 0.2 kg / h, and the calculated leakage intensity I = 0.2 / 50 = 0.004. The leak was classified as medium intensity. The system automatically shortened the scanning interval from 15 minutes to 5 minutes, and after continuous monitoring for 2 hours (preset time period), the leak intensity decreased to 0.0008. Restore the default scan frequency, i.e., restore the 15-minute scan interval.

[0053] By implementing this embodiment, differential absorption lidar 3D scanning can acquire wavelength signals of sulfur and fluorocarbons (SFFCs), accurately capturing SFFC information at nodes. Through concentration field reconstruction and regional integration, SFFC emission flux is quantified and ultimately superimposed onto the total carbon emissions at the node. This fundamentally fills the gap in SFFC monitoring, incorporating SFFC emissions into the calculation of the electrical carbon factor at power grid nodes. This overcomes the shortcomings of existing technologies that only focus on CO2, making the calculation results more consistent with the actual emissions at the nodes and preventing the electrical carbon factor from being underestimated. Simultaneously, real-time electricity and meteorological parameters are collected and rapidly processed through concentration field reconstruction and regional integration to directly calculate the node electrical carbon factor and generate a dynamic heat map. This eliminates the need to rely on historical statistical data or complex forecasts, enabling real-time updates of the electrical carbon factor and accurately capturing changes in carbon flow at power grid nodes. Based on the structural parameters of power grid nodes, a 3D concentration matrix of SFFCs is obtained through concentration field reconstruction using SFFC wavelength signals and power grid node structural parameters. This achieves precise spatial coverage at the node level, rather than regional average calculation, ensuring that the electrical carbon factor truly reflects the actual emission level of the node and overcoming the accuracy limitations of existing methods. Therefore, this invention can improve the accuracy of the assessment of the carbon factor and also reflect the actual emissions of the power grid nodes.

[0054] See Figure 4 This is a schematic diagram of the structure of a power grid node carbon factor monitoring device according to an embodiment of the present invention, comprising: The data acquisition module is used to acquire real-time power consumption, meteorological parameters, and grid node structure parameters of the power grid area to be monitored, and to acquire the wavelength signals of sulfur and fluorine compounds by performing three-dimensional scanning based on differential absorption lidar. The concentration matrix construction module is used to reconstruct the concentration field based on the wavelength signal of the sulfur-fluorine compound and the structural parameters of the power grid node to obtain a three-dimensional concentration matrix of the sulfur-fluorine compound. The emission flux calculation module is used to calculate the emission flux of sulfur and fluorine compounds by performing regional integration based on the three-dimensional concentration matrix of sulfur and fluorine compounds and the meteorological parameters. The node carbon factor calculation module is used to calculate the node carbon factor based on the sulfur and fluorine compound emission flux, the real-time electricity, and the grid node structure parameters. The node electrocarbon factor visualization module is used to generate a dynamic thermogram of the electrocarbon factor by correlating the node electrocarbon factor, the grid node structural parameters, and the three-dimensional concentration matrix of sulfur and fluorine compounds.

[0055] Preferably, the concentration matrix construction module is used to reconstruct the concentration field based on the wavelength signal of the sulfur-fluorinated compound and the structural parameters of the power grid node to obtain a three-dimensional concentration matrix of the sulfur-fluorinated compound, including: The wavelength signal of the sulfur-fluorine compound is filtered and denoised to obtain the processed wavelength signal of the sulfur-fluorine compound; and the intensity of the absorption wavelength signal is determined based on the processed wavelength signal of the sulfur-fluorine compound. Based on the absorption wavelength signal intensity, the preset absorption wavelength baseline calibration data, the reference wavelength signal intensity, and the reference wavelength baseline calibration data, the range-resolved concentration at each three-dimensional scanning angle of the differential absorption lidar is calculated; wherein, the reference wavelength signal intensity represents the echo signal intensity acquired by the differential absorption lidar at a reference wavelength that is not absorbed by sulfur and fluorine compounds; the reference wavelength baseline calibration data represents the preset signal baseline data of the differential absorption lidar in an environment without sulfur and fluorine compounds. Based on the monitoring area boundary and preset spatial resolution in the power grid node structure parameters, a three-dimensional grid system is constructed with the transformer to be monitored in the power grid area to be monitored as the center. Under the aforementioned three-dimensional mesh system, the projection matrix is ​​determined based on the intersection points of the laser beam path of the differential absorption lidar and the three-dimensional mesh system. Iterative algebraic reconstruction is performed based on the distance-resolved concentration, the projection matrix, and the current concentration matrix until the root mean square error of the concentration matrix difference between adjacent iterations converges, resulting in the final three-dimensional concentration matrix of sulfur and fluorine compounds; wherein, the current concentration matrix during the first algebraic reconstruction is the preset initial concentration matrix under the three-dimensional grid system.

[0056] Specifically, the meteorological parameters include wind direction, wind speed, and atmospheric stability parameters.

[0057] Preferably, the emission flux calculation module is used to calculate the sulfur and fluorocarbon emission flux by performing regional integration based on the three-dimensional concentration matrix of sulfur and fluorocarbons and the meteorological parameters, including: Based on the three-dimensional mesh system, the wind direction is converted into a coordinate direction vector; The corresponding horizontal and vertical diffusion coefficients are determined based on the atmospheric stability parameters, and the preset Gaussian plume model is corrected based on the horizontal and vertical diffusion coefficients. In the three-dimensional space corresponding to the three-dimensional mesh system, the cross section perpendicular to the coordinate direction vector is used as the calculation cross section of the modified Gaussian plume model, and the angle between the coordinate direction vector and the normal direction of the calculation cross section is used as the calculation angle. The total regional emission flux is obtained by performing double integral calculations in the modified Gaussian plume model based on the calculated cross section, the calculated angle, and the wind speed. The total emission flux of several regions is calculated based on a preset interval, and the average value of the total emission flux of several regions is taken as the emission flux of sulfur and fluorine compounds.

[0058] Preferably, the node electric carbon factor calculation module is used to calculate the node electric carbon factor based on the sulfur and fluorine compound emission flux, the real-time electricity consumption, and the grid node structural parameters, including: Based on the sulfur and fluorine compound emission flux and the preset collection period duration, the carbon equivalent corresponding to the sulfur and fluorine compound emission flux is calculated. Based on the power supply structure information in the power grid node structure parameters, carbon emissions from non-sulfur and fluorine compound sources are obtained; The total carbon emissions of the grid node to be monitored are calculated by adding the carbon emissions from non-sulfur and fluorine compound sources and the carbon equivalents corresponding to the emission fluxes of sulfur and fluorine compounds. The ratio of total carbon emissions to real-time electricity consumption is used as the node carbon factor.

[0059] Preferably, the node electrocarbon factor visualization module is used to generate a dynamic heat map of the electrocarbon factor by associating the node electrocarbon factor, the power grid node structural parameters, and the three-dimensional concentration matrix of sulfur and fluorine compounds, including: spatially associating the node electrocarbon factor, the three-dimensional concentration matrix of sulfur and fluorine compounds, and the geographical coordinate information of the power grid node according to the geographical coordinate information of the power grid node in the power grid node structural parameters, and constructing a mapping relationship; The values ​​corresponding to the node carbon factor are divided into preset hierarchical intervals, and the geographic coordinate information of the power grid node corresponding to the node carbon factor in different hierarchical intervals is labeled with the same color gradient based on the mapping relationship; wherein, different hierarchical intervals correspond to different color gradients. Extract data from different concentration regions in the three-dimensional concentration matrix of sulfur and fluorine compounds, and label the concentration regions corresponding to the data of different concentration regions with the same color gradient based on the mapping relationship and the geographical coordinate information of the power grid nodes. The node carbon factor is labeled, and the labeled grid node geographic coordinates are labeled in the corresponding labeled concentration area to generate a dynamic heat map of the carbon factor.

[0060] Schematic, it also includes a scanning frequency adjustment module for calculating the leakage intensity based on the ratio of the sulfur and fluorine compound emission flux to the area of ​​the power grid region to be monitored; The leakage intensity is compared with a preset intensity threshold. If the leakage intensity is less than or equal to the intensity threshold, maintain the current scanning frequency of the differential absorption lidar; If the leakage intensity is greater than the intensity threshold, the scanning frequency of the differential absorption lidar is updated to a preset first scanning frequency until the leakage intensity is less than or equal to the intensity threshold within a preset time period.

[0061] This invention provides a power grid node electrocarbon factor monitoring device. In the data acquisition module, real-time power consumption, meteorological parameters, and power grid node structural parameters of the monitored power grid area are acquired, and a three-dimensional scan based on differential absorption lidar is performed to obtain the wavelength signals of sulfur and fluorocarbons (SFFCs). In the concentration matrix construction module, the concentration field is reconstructed based on the SFFC wavelength signals and the power grid node structural parameters to obtain a three-dimensional concentration matrix of SFFCs. In the emission flux calculation module, the emission flux of SFFCs is calculated by regional integration based on the three-dimensional concentration matrix of SFFCs and the meteorological parameters. In the node electrocarbon factor calculation module, the node electrocarbon factor is calculated based on the SFFC emission flux, the real-time power consumption, and the power grid node structural parameters. Finally, in the node electrocarbon factor visualization module, a dynamic heat map of the electrocarbon factor is generated by correlating the node electrocarbon factor, the power grid node structural parameters, and the SFFC three-dimensional concentration matrix.

[0062] By acquiring the wavelength signals of sulfur and fluorocarbons (SFFCs) through differential absorption lidar three-dimensional scanning, the system accurately captures SFFC information at power grid nodes. Through concentration field reconstruction and regional integration, the emission flux of SFFCs is quantified and ultimately superimposed onto the total carbon emissions at the nodes. This fundamentally fills the gap in SFFC monitoring, incorporating SFFC emissions into the calculation of the electrical carbon factor at power grid nodes. This overcomes the limitation of existing technologies that only focus on CO2, making the calculation results more consistent with the actual emission situation at the nodes and preventing the electrical carbon factor from being underestimated. Simultaneously, real-time electricity and meteorological parameters are collected and rapidly processed through concentration field reconstruction and regional integration to directly calculate the node electrical carbon factor and generate a dynamic heat map. This eliminates the need for historical statistical data or complex forecasts, enabling real-time updates of the electrical carbon factor and accurately capturing changes in carbon flow at power grid nodes. Based on the structural parameters of power grid nodes, a three-dimensional concentration matrix of SFFCs is obtained through concentration field reconstruction using the SFFC wavelength signals and the power grid node structural parameters. This achieves precise spatial coverage at the node level, rather than regional averaging, ensuring that the electrical carbon factor truly reflects the actual emission level of the node and overcoming the accuracy limitations of existing methods. Therefore, this invention can improve the accuracy of the assessment of the carbon factor and also reflect the actual emissions of the power grid nodes.

[0063] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0064] Those skilled in the art will understand that, for convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0065] Another embodiment of the present invention provides a terminal device, 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, it implements a method for monitoring the electrocarbon factor at a power grid node as described in the above embodiments. The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0066] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0067] The memory can be used to store the computer program. The processor implements various functions of the terminal device by running or executing the computer program stored in the memory and calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device or other volatile solid-state storage device.

[0068] Another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform a power grid node carbon factor monitoring method as described in the above embodiment.

[0069] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0070] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for monitoring the electrocarbon factor at power grid nodes, characterized in that, include: The system acquires real-time power consumption, meteorological parameters, and grid node structure parameters for the monitored power grid area, and performs three-dimensional scanning based on differential absorption lidar to obtain wavelength signals of sulfur and fluorine compounds. Based on the wavelength signal of the sulfur and fluorine compounds and the structural parameters of the power grid nodes, the concentration field is reconstructed to obtain a three-dimensional concentration matrix of sulfur and fluorine compounds. The emission flux of sulfur and fluorine compounds is calculated by performing regional integration based on the three-dimensional concentration matrix of sulfur and fluorine compounds and the meteorological parameters. The node electrocarbon factor is calculated based on the sulfur and fluorine compound emission flux, the real-time electricity consumption, and the grid node structural parameters. A dynamic thermogram of the electric carbon factor is generated by correlating the node's electric carbon factor, the grid node's structural parameters, and the three-dimensional concentration matrix of sulfur and fluorine compounds.

2. The method for monitoring the electrocarbon factor at power grid nodes as described in claim 1, characterized in that, Based on the wavelength signal of the sulfur and fluorine compounds and the structural parameters of the power grid nodes, a concentration field reconstruction is performed to obtain a three-dimensional concentration matrix of sulfur and fluorine compounds, including: The wavelength signal of the sulfur-fluorine compound is filtered and denoised to obtain the processed wavelength signal of the sulfur-fluorine compound; and the intensity of the absorption wavelength signal is determined based on the processed wavelength signal of the sulfur-fluorine compound. Based on the absorption wavelength signal intensity, the preset absorption wavelength baseline calibration data, the reference wavelength signal intensity, and the reference wavelength baseline calibration data, the range-resolved concentration at each three-dimensional scanning angle of the differential absorption lidar is calculated; wherein, the reference wavelength signal intensity represents the echo signal intensity acquired by the differential absorption lidar at a reference wavelength that is not absorbed by sulfur and fluorine compounds; the reference wavelength baseline calibration data represents the preset signal baseline data of the differential absorption lidar in an environment without sulfur and fluorine compounds. Based on the monitoring area boundary and preset spatial resolution in the power grid node structure parameters, a three-dimensional grid system is constructed with the transformer to be monitored in the power grid area to be monitored as the center. Under the aforementioned three-dimensional mesh system, the projection matrix is ​​determined based on the intersection points of the laser beam path of the differential absorption lidar and the three-dimensional mesh system. Iterative algebraic reconstruction is performed based on the distance-resolved concentration, the projection matrix, and the current concentration matrix until the root mean square error of the concentration matrix difference between adjacent iterations converges, resulting in the final three-dimensional concentration matrix of sulfur and fluorine compounds; wherein, the current concentration matrix during the first algebraic reconstruction is the preset initial concentration matrix under the three-dimensional grid system.

3. The method for monitoring the electrocarbon factor at power grid nodes as described in claim 2, characterized in that, The meteorological parameters include: wind direction, wind speed, and atmospheric stability parameters; Based on the three-dimensional concentration matrix of sulfur and fluorine compounds and the meteorological parameters, the emission flux of sulfur and fluorine compounds is calculated by regional integration, including: Based on the aforementioned three-dimensional mesh system, the wind direction is converted into a coordinate direction vector; The corresponding horizontal and vertical diffusion coefficients are determined based on the atmospheric stability parameters, and the preset Gaussian plume model is corrected based on the horizontal and vertical diffusion coefficients. In the three-dimensional space corresponding to the three-dimensional mesh system, the cross section perpendicular to the coordinate direction vector is used as the calculation cross section of the modified Gaussian plume model, and the angle between the coordinate direction vector and the normal direction of the calculation cross section is used as the calculation angle. The total regional emission flux is obtained by performing double integral calculations in the modified Gaussian plume model based on the calculated cross section, the calculated angle, and the wind speed. The total emission flux of several regions is calculated based on a preset interval, and the average value of the total emission flux of several regions is taken as the emission flux of sulfur and fluorine compounds.

4. The method for monitoring the electrocarbon factor at power grid nodes as described in claim 1, characterized in that, The node electrocarbon factor is calculated based on the sulfur and fluorine compound emission flux, the real-time electricity consumption, and the grid node structural parameters, including: Based on the sulfur and fluorine compound emission flux and the preset collection period duration, the carbon equivalent corresponding to the sulfur and fluorine compound emission flux is calculated. Based on the power supply structure information in the power grid node structure parameters, carbon emissions from non-sulfur and fluorine compound sources are obtained; The total carbon emissions of the grid node to be monitored are calculated by adding the carbon emissions from non-sulfur and fluorine compound sources and the carbon equivalents corresponding to the sulfur and fluorine compound emission fluxes. The ratio of total carbon emissions to real-time electricity consumption is used as the node carbon factor.

5. The method for monitoring the electrocarbon factor at power grid nodes as described in claim 1, characterized in that, A dynamic thermogram of the electric carbon factor is generated by correlating the nodal electric carbon factor, the grid nodal structural parameters, and the three-dimensional concentration matrix of sulfur and fluorine compounds, including: Based on the power grid node geographical coordinate information in the power grid node structural parameters, the node electrocarbon factor, the three-dimensional concentration matrix of sulfur and fluorine compounds, and the power grid node geographical coordinate information are spatially correlated to construct a mapping relationship; The values ​​corresponding to the node carbon factor are divided into preset hierarchical intervals, and the geographic coordinate information of the power grid node corresponding to the node carbon factor in different hierarchical intervals is labeled with the same color gradient based on the mapping relationship; wherein, different hierarchical intervals correspond to different color gradients. Extract data from different concentration regions in the three-dimensional concentration matrix of sulfur and fluorine compounds, and label the concentration regions corresponding to the data of different concentration regions with the same color gradient based on the mapping relationship and the geographical coordinate information of the power grid nodes. The node carbon factor is labeled, and the labeled grid node geographic coordinates are labeled in the corresponding labeled concentration area to generate a dynamic heat map of the carbon factor.

6. The method for monitoring the electrocarbon factor at a power grid node as described in claim 1, characterized in that, Also includes: The leakage intensity is calculated based on the ratio of the sulfur and fluorine compound emission flux to the area of ​​the power grid region to be monitored; The leakage intensity is compared with a preset intensity threshold. If the leakage intensity is less than or equal to the intensity threshold, maintain the current scanning frequency of the differential absorption lidar; If the leakage intensity is greater than the intensity threshold, the scanning frequency of the differential absorption lidar is updated to a preset first scanning frequency until the leakage intensity is less than or equal to the intensity threshold within a preset time period.

7. The method for monitoring the electrocarbon factor at power grid nodes as described in claim 1, characterized in that, The real-time electricity consumption is obtained through a smart meter; the meteorological parameters are obtained through a weather station. The timestamps of the data collected by the smart meters, weather stations, and differential absorption lidar are consistent.

8. A power grid node carbon factor monitoring device, characterized in that, include: The data acquisition module is used to acquire real-time power consumption, meteorological parameters, and grid node structure parameters of the power grid area to be monitored, and to acquire the wavelength signals of sulfur and fluorine compounds by performing three-dimensional scanning based on differential absorption lidar. The concentration matrix construction module is used to reconstruct the concentration field based on the wavelength signal of the sulfur-fluorine compound and the structural parameters of the power grid node to obtain a three-dimensional concentration matrix of the sulfur-fluorine compound. The emission flux calculation module is used to calculate the emission flux of sulfur and fluorine compounds by performing regional integration based on the three-dimensional concentration matrix of sulfur and fluorine compounds and the meteorological parameters. The node carbon factor calculation module is used to calculate the node carbon factor based on the sulfur and fluorine compound emission flux, the real-time electricity, and the grid node structure parameters. The node electrocarbon factor visualization module is used to generate a dynamic thermogram of the electrocarbon factor by correlating the node electrocarbon factor, the grid node structural parameters, and the three-dimensional concentration matrix of sulfur and fluorine compounds.

9. A terminal device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements a method for monitoring the electrical carbon factor at a power grid node as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform a method for monitoring the carbon factor of a power grid node as described in any one of claims 1 to 7.

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