A visualized carbon emission management method, device and medium

Abstract

The application discloses a visual carbon emission management method and device and medium, and relates to the technical field of visualization management and control, which comprises the following steps: collecting carbon emission data and preprocessing; inputting the carbon emission data into a digital twin model to output a dynamic carbon flow topology graph; spatially registering the dynamic carbon flow topology graph with coal-fired equipment of a coal-fired power plant, counting the carbon emission change slope of each coal-fired equipment at fixed time intervals, and outputting a thermal rendering graph; performing halo color identification and carbon flow equipment correlation analysis on the coal-fired equipment in the thermal rendering graph to generate equipment adjustment instructions; executing the equipment adjustment instructions, re-collecting the carbon emission data, judging the execution effect of the equipment adjustment instructions, superimposing the execution effect, the dynamic carbon flow topology graph and the thermal rendering graph in a digital twin scene, and forming a visual carbon emission management graph. The application realizes accurate positioning and closed-loop regulation and control of carbon emission through the cooperation of halo dynamic rendering and three-flow reverse analysis double mechanisms.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of visual management, and in particular to a visual carbon emission management method, device and medium. BACKGROUND

[0002] Current carbon emission management technology is evolving towards multi-source heterogeneous data fusion and dynamic visualization. The mainstream scheme relies on Internet of Things sensor network to collect multi-dimensional data such as fuel combustion and power consumption, combines deep learning algorithm to build carbon emission accounting model, among which digital twin is gradually applied to full-process carbon footprint mapping of thermal power plants, time-space database provides basic framework for dynamic analysis of key parameters, lightweight convolutional neural network (CNN) realizes preliminary identification of carbon emission hotspots, optical flow method is gradually introduced into dynamic tracking of material and energy flow, and multi-modal feature fusion algorithm promotes the evolution of carbon management from static report to dynamic topology presentation. Edge computing terminal deployment significantly improves real-time performance, and three-dimensional rendering provides technical support for carbon emission spatial positioning.

[0003] However, the existing technology has limitations in dynamic coupling accuracy and verification mechanism. Traditional schemes mostly rely on independent data processing modules, and do not establish real-time interactive coupling mechanism for three-flow (material / energy / carbon) data, which weakens the spatio-temporal correlation between carbon emission sources and equipment operating state. Halo rendering mostly uses fixed threshold alarm, lacks progressive visualization expression based on slope trend continuity, and causes insufficient confidence in tracing high-emission equipment. The verification link generally lacks multi-layer fusion mechanism synchronized in time and space, and the regulation effect evaluation relies on offline data comparison, making it difficult to achieve precise closed-loop management of emission hotspots. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a visual carbon emission management method to solve the problem of limitations in dynamic coupling accuracy and verification mechanism.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a visualized carbon emission management method, which comprises collecting and preprocessing carbon emission data, wherein the carbon emission data comprises material flow data, energy flow data and carbon flow data; inputting the preprocessed carbon emission data into a digital twin model, performing dynamic visualized conversion on the carbon emission data by the digital twin model, dynamically coupling each dynamic visualized conversion result, and outputting a dynamic carbon flow topology graph; spatially registering the dynamic carbon flow topology graph with coal-fired equipment of a coal-fired power plant, statistically calculating carbon emission change slopes of each coal-fired equipment at fixed time intervals, performing halo rendering on the coal-fired equipment based on the carbon emission change slopes, and outputting a thermal rendering graph; performing halo color identification and carbon flow equipment correlation analysis on the coal-fired equipment in the thermal rendering graph, outputting halo equipment coordinates, and performing reverse three-flow coupling feature analysis on the dynamic carbon flow topology graph according to the halo equipment coordinates to generate equipment adjustment instructions; re-collecting the carbon emission data by executing the equipment adjustment instructions, judging the execution effect of the equipment adjustment instructions, superimposing the execution effect, the dynamic carbon flow topology graph and the thermal rendering graph in a digital twin scene, and forming a visualized carbon emission management graph.

[0008] As a preferred scheme of the visualized carbon emission management method, the step of inputting the preprocessed carbon emission data into the digital twin model, performing dynamic visualized conversion on the carbon emission data by the digital twin model, and dynamically coupling each dynamic visualized conversion result to output the dynamic carbon flow topology graph comprises the following steps.

[0009] The preprocessed carbon emission data is input into the digital twin model, and the digital twin model performs dynamic visualized conversion on the material flow data, the energy flow data and the carbon flow data in the carbon emission data respectively.

[0010] The coal conveying amount and the carbon content in the material flow data are subjected to spatio-temporal frequency domain feature deconstruction to generate a coal conveying amount mapping set.

[0011] The steam pressure value and the temperature value in the energy flow data are subjected to multi-physical field coupling to generate a steam energy mapping set.

[0012] The carbon dioxide concentration and the flue gas motion trajectory in the carbon flow data are subjected to turbulent diffusion simulation to generate a carbon diffusion mapping set.

[0013] The digital twin model performs real-time dynamic coupling on the coal conveying amount mapping set, the steam energy mapping set and the carbon diffusion mapping set to output the dynamic carbon flow topology graph.

[0014] As a preferred scheme of the visualized carbon emission management method, the step of spatially registering the dynamic carbon flow topology graph with the coal-fired equipment of the coal-fired power plant and statistically calculating the carbon emission change slopes of each coal-fired equipment at fixed time intervals comprises the following steps.

[0015] aligning the dynamic carbon flow topology graph with a three-dimensional image of the coal-fired equipment of the coal-fired power plant in spatial coordinates to generate a registered topology graph;

[0016] extracting carbon flow density data of each coal-fired equipment from the registered topology graph and performing physical quantity mapping conversion on the carbon flow density data to output a carbon emission intensity sequence;

[0017] performing adjacent point difference operation on each carbon emission intensity sequence at a fixed time interval to generate a carbon emission change slope.

[0018] As a preferred scheme of the visualized carbon emission management method, wherein: the coal-fired equipment is rendered with a halo based on the carbon emission change slope, and a thermal rendering map is output, and the specific steps are,

[0019] trend persistence determination is performed on the carbon emission change slope;

[0020] When the carbon emission change slope is positive in consecutive periods, the coal-fired equipment is marked as having an upward trend in carbon emission;

[0021] When the carbon emission change slope is negative in consecutive periods, the coal-fired equipment is marked as having a downward trend in carbon emission;

[0022] Based on the trend determination result, the coal-fired equipment marked as having an upward trend in carbon emission is rendered with a color gradually strong halo, and the coal-fired equipment marked as having a downward trend in carbon emission is rendered with a color gradually weak halo, and a thermal rendering map is output.

[0023] As a preferred scheme of the visualized carbon emission management method, wherein: the coal-fired equipment in the thermal rendering map is rendered with a halo color and analyzed in association with the carbon flow equipment, and a halo equipment coordinate is output, and the specific steps are,

[0024] extracting pixel regions of color gradually strong halo and color gradually weak halo from the thermal rendering map, mapping pixel region coordinates of the pixel regions to a physical coordinate system of the coal-fired equipment, and determining a halo physical coordinate range;

[0025] performing geometric overlap detection on the halo physical coordinate range and the spatial boundary of the coal-fired equipment to identify an overlapping coal-fired equipment identifier;

[0026] verifying the spatial association of the coal-fired equipment identifier with the carbon flow equipment in the dynamic carbon flow topology graph to output a verified coal-fired equipment identifier;

[0027] Performing equipment coordinate query based on the verified coal-fired equipment identifier to output a halo equipment coordinate.

[0028] As a preferred scheme of the visualized carbon emission management method, wherein: the reverse three-flow coupling feature analysis of the dynamic carbon flow topology graph according to the halo device coordinates is performed to generate device adjustment instructions, and the specific steps are,

[0029] The associated area of the halo device coordinates is located from the dynamic carbon flow topology graph, the transport capacity change gradient, the energy oscillation amplitude and the diffusion turbulence intensity in the associated area are extracted, and the three-flow coupling feature data set is integrated and output;

[0030] The multi-modal offset analysis is performed on the three-flow coupling feature data set, the time sequence mismatch amount of the transport capacity change gradient and the energy oscillation amplitude is detected, and the feature offset index is output;

[0031] The feature offset index is compared with the trend consistency of the carbon emission change slope, and the abnormal type identifier is output;

[0032] The abnormal type identifier is matched with the preset device regulation strategy to generate the device adjustment instruction.

[0033] As a preferred scheme of the visualized carbon emission management method, wherein: the reverse three-flow coupling feature analysis of the dynamic carbon flow topology graph according to the halo device coordinates is performed to generate device adjustment instructions, and the specific steps are,

[0034] The device adjustment instruction is executed, the carbon emission data is re-collected, and the updated carbon emission data set is output;

[0035] The thermal rendering graph is updated based on the updated carbon emission data set, and the updated thermal rendering graph is output;

[0036] The updated thermal rendering graph and the color intensity halo intensity value of the thermal rendering graph at the same coal-fired device coordinates are compared, and if the halo intensity value of the updated thermal rendering graph is lower than the halo intensity value of the thermal rendering graph, it is determined that the device adjustment instruction execution effect is effective.

[0037] As a preferred scheme of the visualized carbon emission management method, wherein: the execution effect, the dynamic carbon flow topology graph and the thermal rendering graph are superimposed in the digital twin scene to form the visualized carbon emission management graph, and the specific steps are,

[0038] The execution effect, the dynamic carbon flow topology graph and the thermal rendering graph are spatially aligned in the digital twin scene, and the spatial alignment data is output;

[0039] The visualized superposition of each spatial alignment data is formed to form the visualized carbon emission management graph.

[0040] In a second aspect, the present invention provides a computer device including a memory and a processor, the memory storing a computer program, wherein: when the computer program is executed by the processor, it implements any step of the visualized carbon emission management method as described in the first aspect of the present invention.

[0041] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the visualized carbon emission management method as described in the first aspect of the present invention.

[0042] The beneficial effects of this invention are as follows: Through the synergistic effect of dynamic halo rendering and three-flow reverse analysis, precise location and closed-loop control of carbon emissions are achieved. Halo rendering is driven by the continuous determination of the slope trend of carbon emission changes. Devices with upward trends are marked with gradually increasing color halos and output as thermal rendering maps. Abstract slope data is transformed into spatially visualized thermal distribution, intuitively presenting the spatiotemporal evolution path of high-emission devices and supporting rapid location of problem sources. Reverse three-flow coupling feature analysis is triggered by the coordinates of the haloed devices, extracting the temporal mismatch between the gradient of transport volume changes and the amplitude of energy oscillations in the dynamic carbon flow topology map. Combined with cross-modal comparison of feature offset indicators and slope trends, equipment adjustment instructions for abnormal material or energy flows are generated, enabling root cause diagnosis and precise control of multi-physics coupling anomalies. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Fig. 1 A flowchart for a visualized approach to carbon emission management.

[0045] Fig. 2 This is a flowchart for outputting a dynamic carbon flow topology diagram.

[0046] Fig. 3 The flowchart for outputting the thermal rendering map.

[0047] Fig. 4 This is a flowchart for analyzing the reverse three-flow coupling characteristics. Detailed Implementation

[0048] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0049] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one skilled in the art that the present application can be practiced without the specific details set forth in this description. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to unnecessarily obscure aspects of the present application.

[0050] It should also be noted that, as used herein, "the embodiment" refers to a particular element described above. This example can be mixed and matched with other examples described herein, or can be used alone.

[0051] Reference Figs. 1-4 For one embodiment of the present application, the embodiment provides a visualized carbon emission management method, comprising the following steps:

[0052] S1, collect carbon emission data and perform preprocessing.

[0053] The carbon emission data includes material flow data, energy flow data and carbon flow data;

[0054] Specifically, the material flow data is collected by deploying a mass flow meter at the coal inlet of the boiler, and the flow value, coal mass and carbon content percentage are recorded in real time; the energy flow data is collected by synchronously deploying a steam pipeline pressure sensor and a temperature sensor, and the steam pressure value and temperature value are recorded; the carbon flow data is collected by deploying a flue gas analyzer, and the dynamic changes of carbon dioxide concentration and flue gas flow in the flue gas are monitored in real time;

[0055] The preprocessing includes data cleaning, timestamp alignment and data format standardization;

[0056] The data cleaning filters and corrects the abnormal values in the material flow data, energy flow data and carbon flow data: for the material flow data, it is detected whether the instantaneous flow value exceeds the rated range of the equipment (the rated range of the equipment is obtained by the calibration parameters of the equipment manual of the mass flow meter), and if it exceeds, it is rejected; for the energy flow data output by the pressure sensor, it is detected whether the pressure value has a step mutation, and if it has a step mutation, it is subjected to Kalman filter smoothing processing; for the carbon flow data output by the flue gas analyzer, it is detected whether the carbon dioxide concentration value is continuously zero, and if it is continuously zero, it is marked as sensor failure and triggers redundant device switching;

[0057] The carbon emission data after data cleaning is subjected to timestamp alignment, and the timestamp alignment synchronizes the clocks of each sensor based on high-precision network time protocol (NTP): each piece of material flow data record is marked with a millisecond-level timestamp, and the energy flow data and carbon flow data at the same time are synchronously matched;

[0058] The timestamp-aligned carbon emission data is subjected to data format standardization processing, which converts heterogeneous data into a unified specification: the percentage of coal carbon content in the material flow data is converted to decimal form, the temperature value in the energy flow data is converted from Fahrenheit to Celsius, and the flue gas flow in the carbon flow data is converted from feet per minute to meters per second, generating a JSON structured data package that meets the input specification of the digital twin model.

[0059] S2, input the preprocessed carbon emission data into the digital twin model, the digital twin model converts the carbon emission data into dynamic visualizations, and dynamically couples the results of the dynamic visualizations to output a dynamic carbon flow topology map;

[0060] The preprocessed carbon emission data is input into the digital twin model, which converts the material flow data, energy flow data and carbon flow data in the carbon emission data into dynamic visualizations respectively;

[0061] Specifically, the digital twin model maps the coal delivery volume and carbon content in the material flow data into three-dimensional visual dynamic arrows, where the arrow length reflects the delivery volume and the arrow color reflects the carbon content; maps the steam pressure value and temperature value in the energy flow data into three-dimensional visual pulsating light strips, where the light strip brightness reflects the pressure value and the flicker frequency reflects the temperature value; maps the carbon dioxide concentration and flue gas flow in the carbon flow data into three-dimensional visual dynamic particle groups, where the particle group density reflects the concentration and the particle group motion trajectory reflects the flue gas flow direction; according to the timestamp order, the dynamic changes of the visual elements are updated in real time to ensure that the visualization results are synchronized with the actual operation state of the coal-fired power plant; after dynamic visualization conversion, the material flow visualization dataset, energy flow visualization dataset and carbon flow visualization dataset are formed, which respectively contain the rendering parameters of dynamic arrows, pulsating light strips and dynamic particle groups, providing input for subsequent mapping set generation;

[0062] Further, the digital twin model is constructed by integrating real-time monitoring data, historical operation records and multidisciplinary simulation of the physical entity, and the core is to map the physical properties, operating state and environmental interaction of the entity object to the virtual space; wherein the physical properties are obtained from the technical requirements provided by the manufacturer of the coal-fired equipment (such as boiler volume, pipe diameter and material thermal conductivity), the operating state is obtained from the real-time monitoring carbon emission data (such as material flow data, energy flow data and carbon flow data), and the environmental interaction is obtained through multi-physical field coupling simulation (such as thermodynamic simulation of the relationship between boiler combustion and steam generation and fluid mechanics simulation of the flow of flue gas in the pipe); the specific process includes: collecting carbon emission data (such as material flow data, energy flow data and carbon flow data) of the power plant entity based on the sensor network, combining the construction drawings and process requirements of the power plant entity to generate a power plant geometric model; simulating the behavior characteristics of the carbon emission entity under different working conditions through multi-physical field coupling (such as thermodynamics, fluid mechanics and structural mechanics) to form dynamic response rules; synchronizing the carbon emission data and simulation results using time stamp alignment to ensure that the power plant geometric model and entity state strictly correspond in the time dimension; converting the carbon emission data into an interactive three-dimensional dynamic scene through visual rendering to continuously reflect the full life cycle evolution of the power plant entity.

[0063] Temporal and spatial frequency domain feature deconstruction is performed on the coal conveying amount and carbon content in the material flow data to generate a coal conveying amount mapping set;

[0064] Specifically, the coal conveying amount and the carbon content are arranged in time sequence data according to the time stamp sequence, the change rate of the conveying amount per second is counted, the high-frequency fluctuation and the low-frequency trend of the conveying amount are identified, the low-frequency characteristics reflecting the stable input of the coal are retained, the high-frequency noise caused by mechanical vibration is eliminated, the coal conveying amount is mapped into the three-dimensional space of the coal-fired power plant combined with the position coordinates of the mass flow meter of the boiler coal inlet, the dynamic arrow track along the coal conveying belt path is generated, and the arrow length is linearly scaled according to the conveying amount (for example, 1 kilogram per second corresponds to 1 unit length); the carbon content is converted into a color gradient, and the carbon content from 0.1 to 0.9 corresponds to a color gradient from light gray to dark black, which is applied to the surface rendering of the dynamic arrow; the periodic characteristics of the conveying amount and the carbon content are extracted by fast Fourier transform to identify the regularity mode of the coal input (such as the periodic fluctuation every hour), specifically, the time sequence data of the coal conveying amount and the carbon content are frequency domain converted by fast Fourier transform to obtain the frequency spectrum power density distribution, the fundamental frequency and the harmonic frequency component corresponding to the peak value of the frequency spectrum power density are determined from the frequency spectrum power density distribution, and whether the fundamental frequency and the harmonic frequency component match the coal equipment operation is verified according to the coal-fired power plant combustion cycle (such as the fixed operation interval of the boiler coal adding and ash cleaning), the periodic fluctuation (such as the peak value every hour) that matches the coal equipment operation is marked as the regularity mode, and the regularity mode is embedded into the animation parameters of the dynamic arrow, so that the arrow presents a periodic stretching effect in the three-dimensional space, forming a coal conveying amount mapping set, the coal conveying amount mapping set includes the length, color, track and animation parameters of the dynamic arrow, and is stored in JSON format with time stamp and space coordinates;

[0065] The steam pressure value and the temperature value in the energy flow data are subjected to multi-physical field coupling to generate a steam energy mapping set;

[0066] The steam pressure value and the temperature value in the energy flow data are arranged in time sequence data in time stamp order, physical field correlation analysis is performed on the time sequence data, the consistency of the change direction of the steam pressure value and the temperature value at the same time point is quantified by analyzing the collaborative fluctuation degree of the steam pressure value and the temperature value in the time sequence: when the steam pressure value rises and the temperature value rises synchronously, the correlation tends to 1, when the steam pressure value decreases and the temperature value decreases synchronously, the correlation tends to -1, and when there is no regular fluctuation, the correlation tends to 0; finally, the correlation coefficient between the steam pressure value and the temperature value in the interval [-1, 1] is output, and the collaborative change rule of the steam pressure value and the temperature value in the time dimension is determined; by taking the three-dimensional path of the steam pipeline as the trajectory baseline of the pulsating light band, the position and direction of the pulsating light band are accurately fitted to the three-dimensional space shape of the steam pipeline, so that the steam pressure value is mapped to the brightness of the pulsating light band, and the pressure value from low to high (for example, 100 kilopascals to 1000 kilopascals) corresponds to the linear gradient of the brightness from dark to bright; the temperature value is mapped to the flicker frequency of the pulsating light band, and the temperature value from low to high (for example, 100 degrees Celsius to 500 degrees Celsius) corresponds to the flicker frequency from slow to fast; the dynamic changes of the steam pressure value and the temperature value are converted into the pulsating trajectory of the light band, the pulsating trajectory extends along the three-dimensional path of the steam pipeline, reflects the actual flow direction of the steam, and outputs the steam energy mapping set, which includes the brightness, flicker frequency, trajectory and animation parameters of the pulsating light band;

[0067] The turbulent diffusion simulation is performed on the carbon dioxide concentration and the flue gas movement trajectory in the carbon flow data to generate a carbon diffusion mapping set.

[0068] The turbulent diffusion simulation is performed on the carbon dioxide concentration and the flue gas movement trajectory in the carbon flow data to generate a carbon diffusion mapping set.

[0069] The digital twin model dynamically couples the coal conveying amount mapping set, the steam energy mapping set and the carbon diffusion mapping set in real time, and outputs a dynamic carbon flow topology diagram.

[0070] Specifically, the coal conveying amount mapping set, the steam energy mapping set, and the carbon diffusion mapping set are spatially aligned, the coordinates of the dynamic arrows, the pulsating light bands, and the dynamic particle groups are mapped to the three-dimensional space of the coal-fired power plant, ensuring that the arrows are precisely distributed along the coal conveying belt, the light bands are precisely distributed along the steam pipeline, and the particle groups are precisely distributed along the flue gas pipeline; the coal conveying amount mapping set, the steam energy mapping set, and the carbon diffusion mapping set are time-synchronized, based on millisecond-level timestamps, the animation update frequency of the visualization elements of the coal conveying amount mapping set, the steam energy mapping set, and the carbon diffusion mapping set is coordinated, ensuring that the dynamic arrows, light bands, and particle groups reflect the operating state of the coal-fired power plant at the same time point; coupling process analyzes the physical correlation of the coal conveying amount mapping set, the steam energy mapping set, and the carbon diffusion mapping set, by extracting the time series data of the coal conveying amount mapping set, the steam energy mapping set, and the carbon diffusion mapping set, the correlation between the coal conveying amount, the steam pressure value, and the carbon dioxide concentration at the same time is analyzed, for example, the correlation between the increase of the coal conveying amount and the increase of the steam pressure and the increase of the carbon dioxide concentration, by adjusting the superimposed transparency of the visualization elements (for example, reducing the transparency when the particle group covers the light band), the visual effect of the fusion of the three is enhanced; the digital twin model synthesizes the dynamic arrows, pulsating light bands, and dynamic particle groups in real time in the three-dimensional rendering engine, generates a dynamic carbon flow topology diagram, the dynamic carbon flow topology diagram shows the coordinated changes of the material flow, energy flow, and carbon flow, and is stored as an interactive rendering file containing timestamps, spatial coordinates, and animation parameters.

[0071] S3, spatially register the dynamic carbon flow topology diagram with the coal-fired equipment of the coal-fired power plant, statistically calculate the carbon emission change slope of each coal-fired equipment at fixed time intervals, perform halo rendering on the coal-fired equipment based on the carbon emission change slope, and output a thermal rendering diagram;

[0072] The dynamic carbon flow topology diagram is spatially aligned with the three-dimensional image of the coal-fired equipment of the coal-fired power plant, and a registered topology diagram is generated;

[0073] Specifically, the three-dimensional image of the coal-fired equipment of the coal-fired power plant is generated based on construction drawings of the boiler, steam turbine and flue gas treatment equipment, contains position coordinates and spatial boundaries of the coal-fired equipment, and is stored in a three-dimensional CAD file format; the JSON rendering file of the dynamic carbon flow topology graph and the three-dimensional CAD file of the coal-fired equipment are loaded into the central processing center; the central processing center extracts the spatial coordinates of the dynamic arrows (along the coal conveying belt), the pulsating light bands (along the steam pipeline) and the dynamic particle groups (along the flue gas pipeline) in the dynamic carbon flow topology graph, and the spatial coordinates of the boiler coal inlet, steam pipeline and flue gas pipeline in the three-dimensional image of the coal-fired equipment; the alignment process is based on the least squares method, and the coordinate transformation matrix of the dynamic carbon flow topology graph and the three-dimensional image of the coal-fired equipment is calculated to ensure that the trajectories of the dynamic arrows, the pulsating light bands and the dynamic particle groups accurately correspond to the actual positions of the boiler, the steam turbine and the flue gas treatment equipment, forming a registered topology graph, which contains rendering parameters of the dynamic carbon flow topology graph and three-dimensional spatial coordinates of the coal-fired equipment, is stored in JSON format, with a timestamp and spatial coordinates after alignment, providing input for subsequent extraction of carbon flow density data.

[0074] The carbon flow density data of each coal-fired equipment is extracted from the registered topology graph, and the carbon flow density data is converted by physical quantity mapping to output a carbon emission intensity sequence;

[0075] Specifically, the dynamic particle group region overlapping with the spatial coordinates of the coal-fired equipment in the registered topology graph is identified, specifically the particle group near the boiler coal inlet (associated material flow), the particle group near the steam pipeline (associated energy flow) and the particle group near the flue gas pipeline (associated carbon flow); for each coal-fired equipment, the average carbon dioxide concentration and flue gas flow of the particle group in the spatial volume of the coal-fired equipment are counted, and the carbon flow density data is calculated; the physical quantity mapping converts the carbon flow density data to carbon emission intensity, and the normalization range is 0 to 1, based on the maximum and minimum carbon flow density values (for example, the maximum value of 100 kg per cubic meter corresponds to 1, and the minimum value of 0 corresponds to 0) of the historical operation data of the coal-fired power plant; the conversion process preserves the timestamp order to ensure that the carbon emission intensity value is generated for each coal-fired equipment every second; the carbon emission intensity values are sorted according to the acquisition order of the carbon flow density data, and a carbon emission intensity sequence is output;

[0076] The carbon flow density calculation formula is,

[0077] ;

[0078] Wherein, represents the carbon flow density of the coal-fired equipment, represents the spatial volume of the coal-fired equipment, represents the start time of the fixed time period, represents the end time of the fixed time period, represents the carbon dioxide concentration of the dynamic particle group changing with time, represents the smoke flow rate of the dynamic particle group changing over time;

[0079] The carbon emission intensity sequence is subjected to adjacent point difference operation at fixed time intervals to generate a carbon emission change slope, and the carbon emission change slope calculation formula is,

[0080] ;

[0081] wherein, represents an identifier of each time point in the fixed time interval, represents the carbon emission change slope of the th time point, represents the carbon emission intensity value of the th time point, represents the carbon emission intensity value of the th time point, represents the fixed time interval.

[0082] The trend persistence of the carbon emission change slope is determined, and when the carbon emission change slope is positive in consecutive time periods, the coal-fired equipment is marked as having a carbon emission rising trend, and when the carbon emission change slope is negative in consecutive time periods, the coal-fired equipment is marked as having a carbon emission falling trend;

[0083] Specifically, the carbon emission change slope of each coal-fired equipment is extracted in chronological order, and the slope signs of consecutive multiple (such as 3) time windows are checked; for each coal-fired equipment, if the slopes of the consecutive multiple time windows are all positive (greater than 0), it is determined that the coal-fired equipment has a carbon emission rising trend in the current time period; if the slopes of the consecutive multiple time windows are all negative (less than 0), it is determined that the coal-fired equipment has a carbon emission falling trend; if the slope signs are inconsistent (mixed positive and negative or containing zero), it is marked as having no obvious trend;

[0084] Based on the trend determination result, the coal-fired equipment marked as having a carbon emission rising trend is subjected to color gradually strong halo rendering, and the coal-fired equipment marked as having a carbon emission falling trend is subjected to color gradually weak halo rendering, and a heat rendering map is output;

[0085] The halo rendering is based on the three-dimensional image of the coal-fired equipment and the registered topological map. The JSON file of the registered topological map and the three-dimensional CAD file of the coal-fired equipment are loaded for three-dimensional rendering. For the coal-fired equipment marked as a rising trend of carbon emissions, a red gradually strong halo is added to the surface of the three-dimensional image of the coal-fired equipment, and the intensity of the red halo linearly increases with the absolute value of the slope of the carbon emission change (for example, the slope from 0.1 to 1 corresponds to the halo intensity from 10% to 100%). For the coal-fired equipment marked as a downward trend of carbon emissions, a blue gradually weak halo is added, and the intensity of the blue halo linearly decreases with the absolute value of the slope (for example, the slope from -0.1 to -1 corresponds to the halo intensity from 100% to 10%). For the coal-fired equipment without obvious trend, no halo is added, and the original surface rendering of the coal-fired equipment is retained. The rendering process ensures that the halo effect is visually coordinated with the dynamic arrows, pulsating light strips and dynamic particle groups of the registered topological map, avoids blocking the topological map elements by adjusting the halo transparency (for example, set to 50%), generates a heat rendering map, and stores it as an interactive rendering file with a timestamp and spatial coordinates, providing input for subsequent high-carbon emission trend equipment regulation.

[0086] S4, halo color recognition and carbon flow equipment correlation analysis of the coal-fired equipment in the heat rendering map are performed, and halo equipment coordinates are output, and reverse three-flow coupling feature analysis of the dynamic carbon flow topological map is performed according to the halo equipment coordinates, to generate equipment adjustment instructions;

[0087] The pixel regions of the gradually strong halo and the gradually weak halo are extracted from the heat rendering map, the pixel region coordinates of the pixel regions are mapped to the physical coordinate system of the coal-fired equipment, and the halo physical coordinate range is determined;

[0088] Specifically, the halo color recognition loads the JSON rendering file of the heat rendering map, extracts the pixel data in the JSON rendering file, and identifies the pixel regions of the red gradually strong halo and the blue gradually weak halo. The pixel region extraction identifies the RGB color through an image segmentation method, and decomposes the heat rendering map into a red halo pixel region and a blue halo pixel region. The two-dimensional pixel coordinates of the pixel regions are mapped to the physical coordinate system of the coal-fired equipment, the two-dimensional pixel coordinates are converted into three-dimensional space coordinates through the projection transformation of the heat rendering map, the three-dimensional space coordinates are determined according to the three-dimensional space coordinates, the three-dimensional space coordinate range of the red gradually strong halo and the blue gradually weak halo is determined, and the three-dimensional space coordinate range is stored in JSON format with a timestamp, providing input for subsequent geometric overlap detection;

[0089] Geometric overlap detection is performed on the halo physical coordinate range and the spatial boundary of the coal-fired equipment, and the coal-fired equipment identifiers with overlap are identified;

[0090] Specifically, the geometric overlap detection loads JSON files of the light halo physical coordinate range and the spatial boundary of the coal-fired equipment to the central processing center; for each light halo physical coordinate range, the three-dimensional coordinate range is compared with the spatial boundary of each coal-fired equipment (boiler, steam turbine and flue gas treatment equipment) one by one to check whether there is an overlap; if there is an overlap, it is confirmed that the light halo coordinate range is related to the coal-fired equipment, and the coal-fired equipment identifier (for example, boiler number B001 and steam turbine number T001) of the coal-fired equipment is extracted;

[0091] The coal-fired equipment identifier is verified for spatial correlation with the carbon flow equipment in the dynamic carbon flow topology map, and the verified coal-fired equipment identifier is output;

[0092] Specifically, for each coal-fired equipment identifier, the spatial coordinates and boundary data in the three-dimensional CAD file of the coal-fired equipment are extracted, and the spatial coordinates of the dynamic arrow, the pulsating light band and the dynamic particle group in the dynamic carbon flow topology map are extracted at the same time; the dynamic arrow, the pulsating light band and the dynamic particle group are unified to the coal-fired equipment physical coordinate system through the coordinate transformation matrix, and whether the dynamic arrow trajectory accurately matches the coal belt path, whether the pulsating light band trajectory fits the steam pipe direction and whether the dynamic particle group trajectory is distributed along the flue are compared one by one; when the spatial coordinates of the dynamic arrow, the pulsating light band and the dynamic particle group are completely matched with the position of the coal-fired equipment, it is confirmed that the coal-fired equipment identifier passes the spatial correlation verification, and the verified coal-fired equipment identifier is output.

[0093] Based on the verified coal-fired equipment identifier, equipment coordinate query is performed, and the light halo equipment coordinate is output;

[0094] Specifically, the equipment coordinate query extracts the corresponding equipment spatial coordinates from the three-dimensional CAD file for each coal-fired equipment identifier, combines the light halo physical coordinates in the thermal rendering map, filters the coordinates of the red gradually strong light halo or the blue gradually weak light halo corresponding to the verified coal-fired equipment identifier, and outputs the light halo equipment coordinate;

[0095] The associated area of the light halo equipment coordinate is located from the dynamic carbon flow topology map, the delivery volume change gradient of the coal delivery volume mapping set, the energy oscillation amplitude of the steam energy mapping set and the diffusion turbulence intensity of the carbon diffusion mapping set in the associated area are extracted, and the three-flow coupling feature data set is integrated and output;

[0096] Specifically, for each halo device coordinate, a spherical correlation region with a fixed radius is generated with the center point of the halo device coordinate as the center of the sphere; data elements within the spherical correlation region are extracted in the dynamic carbon flow topology graph: the length change rate (length increase / decrease per second) of the dynamic arrow is obtained from the coal delivery volume mapping set to count the delivery volume change gradient; the brightness fluctuation frequency of the pulsating light band is extracted from the steam energy mapping set to count the energy oscillation amplitude; the vortex shedding frequency of the dynamic particle group is read from the carbon diffusion mapping set as the diffusion turbulence intensity; the delivery volume change gradient, energy oscillation amplitude and diffusion turbulence intensity are integrated to output a three-flow coupling feature data set;

[0097] Multi-modal offset analysis is performed on the three-flow coupling feature data set to detect the time sequence mismatch amount of the delivery volume change gradient and the energy oscillation amplitude, and output a feature offset indicator;

[0098] The multi-modal offset analysis extracts the time sequence data of the delivery volume change gradient and the energy oscillation amplitude in time stamp order for each coal-fired equipment identifier, forming two time series; the time sequence mismatch amount is detected by selecting a time window of a continuous fixed time to count the correlation coefficient of the delivery volume change gradient and the energy oscillation amplitude, and the correlation coefficient is determined by point-by-point comparison of the numerical change trend of the two time series; the time delay corresponding to the maximum correlation coefficient is selected as the time sequence mismatch amount, and the time sequence mismatch amount and the partial correlation coefficient of each coal-fired equipment identifier are integrated to output the feature offset indicator;

[0099] The feature offset indicator is compared with the trend consistency of the carbon emission change slope to output an abnormal type identifier;

[0100] Specifically, for each coal-fired equipment identifier, the synergy of the material-energy flow coupling trend direction reflected by the feature offset indicator and the trend direction of the carbon emission change slope is analyzed: when the feature offset indicator continuously indicates that the delivery volume change gradient and the energy oscillation amplitude are in a same-direction enhancement state, and the carbon emission change slope simultaneously presents a continuous upward trend, it is marked as a material flow anomaly; when the feature offset indicator continuously indicates a reverse fluctuation trend, and the carbon emission change slope simultaneously presents a continuous downward trend, it is marked as an energy flow anomaly, and an abnormal type identifier list is output, including the equipment identifier and the corresponding abnormal state classification;

[0101] The abnormal type identifier is matched with the preset equipment control strategy to generate an equipment adjustment instruction;

[0102] Specifically, the preset device regulation strategy is constructed based on the operation requirements of the coal-fired power plant and the characteristic parameters provided by the coal-fired device manufacturer, wherein the material flow anomaly corresponds to a multi-stage regulation mechanism of coal conveying, the combustion efficiency is balanced by dynamically adjusting the speed interval of the coal feeder; the energy flow anomaly corresponds to a steam parameter compensation mechanism, the steam pressure is stabilized by correcting the air distribution valve opening degree according to the boiler thermal characteristic curve; the matching process classifies the abnormal state according to the abnormal type identifier, calls the corresponding strategy template in the device regulation strategy to generate device adjustment instructions, the material flow anomaly triggers the speed interval regulation action of the coal feeder, the energy flow anomaly triggers the compensation correction of the boiler air distribution valve opening degree, and the device adjustment instruction data packet containing the device identifier, the regulation action type and the classification parameters is output.

[0103] S5, execute the device adjustment instruction, re-collect the carbon emission data, judge the execution effect of the device adjustment instruction, superimpose the execution effect, the dynamic carbon flow topology graph and the thermal rendering graph in the digital twin scene to form a visual carbon emission management graph.

[0104] Execute the device adjustment instruction, re-collect the carbon emission data, and output the updated carbon emission data set;

[0105] Specifically, the device adjustment instruction is issued to the coal-fired device through the execution interface of the control center to perform the speed interval regulation of the coal feeder or the compensation of the boiler air distribution valve opening degree; the sensor network re-collection process is triggered after the device adjustment instruction is executed, the mass flow meter re-acquires the coal conveying amount and carbon content data, the pressure sensor and the temperature sensor synchronously collect the updated steam pressure value and temperature value, and the flue gas analyzer re-measures the carbon dioxide concentration and flue gas flow rate to output the updated carbon emission data set;

[0106] Update the thermal rendering graph based on the updated carbon emission data set, and output the updated thermal rendering graph;

[0107] Input the updated carbon emission data set into the digital twin model, and re-execute the dynamic visualization conversion and coupling process: generate a coal conveying amount mapping set (update the dynamic arrow length / color) based on the material flow data set, generate a steam energy mapping set (refresh the brightness / frequency of the pulsating light band) based on the energy flow data set, and generate a carbon diffusion mapping set (particle group density / trajectory redraw) based on the carbon flow data set; real-time dynamic coupling generates a new version of the dynamic carbon flow topology graph and halo rendering, and outputs the updated thermal rendering graph;

[0108] Compare the color intensity values of the same coal-fired device coordinates of the updated thermal rendering graph and the thermal rendering graph, if the color intensity value of the updated thermal rendering graph is lower than that of the thermal rendering graph, it is determined that the execution effect of the device adjustment instruction is effective, and if the color intensity value of the updated thermal rendering graph is higher than that of the thermal rendering graph, it is determined that the execution effect of the device adjustment instruction is ineffective.

[0109] The execution effect, dynamic carbon flow topology graph and heat rendering graph are spatially aligned in the digital twin scene, and spatial alignment data is output;

[0110] Specifically, in the digital twin scene, the execution effect, dynamic carbon flow topology graph and heat rendering graph are unified to the physical coordinate system of the coal-fired equipment through spatial registration: the dynamic arrows / light strips / particle group trajectories of the dynamic carbon flow topology graph are accurately matched with the equipment CAD coordinates, the halo effect of the heat rendering graph is overlapped with the equipment surface coordinates, the execution effect flag is bound to the coal-fired equipment coordinates, and spatial alignment data is output;

[0111] The spatial alignment data is visualized and superimposed to form a visual carbon emission management graph;

[0112] Based on the spatial alignment data package, the visualization synthesis is performed through three-dimensional rendering: the execution effect is converted into a pulse green light ring (valid instruction) or a gray warning ring (invalid instruction) and is superimposed at the equipment coordinates; the dynamic arrows, pulsating light strips and particle groups of the dynamic carbon flow topology graph are rendered according to the original transparency; the halo effect of the heat rendering graph is covered with 50% transparency, ensuring that the pulse light ring is prominently displayed and does not block the carbon flow trajectory and halo, forming a visual carbon emission management graph.

[0113] The embodiment also provides a computer device suitable for the case of the visual carbon emission management method, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize the visual carbon emission management method proposed in the above embodiment.

[0114] The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device. In addition, the input device can be an external keyboard, touchpad or mouse, etc.

[0115] The embodiment also provides a storage medium having a computer program stored thereon, the program being executed by a processor to implement the carbon emission management method for visualization as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0116] To sum up, the present application realizes accurate positioning and closed-loop regulation of carbon emissions through the synergy of halo dynamic rendering and three-flow reverse analysis double mechanisms. The driving halo rendering is determined by the continuity of the slope trend of carbon emission changes, the color gradually strong halo is marked on the equipment with an upward trend, and a heat rendering map is output, abstract slope data is converted into spatial visual heat distribution, and the spatio-temporal evolution path of high-emission equipment is intuitively presented, supporting rapid positioning of the problem source, reverse three-flow coupling feature analysis is triggered by the halo equipment coordinates, the time sequence mismatch amount of the change gradient of the transport capacity and the energy oscillation amplitude in the dynamic carbon flow topology graph is extracted, the cross-modal comparison of the feature offset index and the slope trend is combined, the equipment adjustment instruction for material flow or energy flow abnormalities is generated, and the root cause diagnosis and accurate regulation of multi-physical field coupling abnormalities are realized.

[0117] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit it, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application, which should be covered in the scope of the claims of the present application.

Claims

1. A visual carbon emission management method, characterized in that: include, Carbon emission data is collected and preprocessed; the carbon emission data includes material flow data, energy flow data, and carbon flow data. The preprocessed carbon emission data is input into the digital twin model, which performs dynamic visualization transformation on the carbon emission data and dynamically couples the results of each dynamic visualization transformation to output a dynamic carbon flow topology map. The dynamic carbon flow topology map is spatially registered with the coal-fired equipment of the coal-fired power plant. The carbon emission change slope of each coal-fired equipment is statistically analyzed at fixed time intervals. Based on the carbon emission change slope, the coal-fired equipment is subjected to halo rendering, and a thermal rendering map is output. The halo color recognition and carbon flow equipment correlation analysis are performed on the coal-fired equipment in the thermal rendering map, the coordinates of the halo equipment are output, and the reverse three-flow coupling feature analysis is performed on the dynamic carbon flow topology map according to the coordinates of the halo equipment to generate equipment adjustment instructions. Execute equipment adjustment commands, re-collect carbon emission data, judge the execution effect of equipment adjustment commands, and overlay the execution effect, dynamic carbon flow topology map and thermal rendering map on the digital twin scene to form a visualized carbon emission management map. The process involves inputting preprocessed carbon emission data into a digital twin model, which then performs dynamic visualization transformations on the carbon emission data and dynamically couples the results to output a dynamic carbon flow topology map. The specific steps are as follows: The preprocessed carbon emission data is input into the digital twin model, which then performs dynamic visualization transformations on the material flow data, energy flow data, and carbon flow data within the carbon emission data. Spatiotemporal frequency domain feature deconstruction is performed on the coal transport volume and carbon content in the material flow data to generate a coal transport volume mapping set; Multiphysics coupling is performed on the steam pressure and temperature values ​​in the energy flow data to generate a steam energy mapping set; Turbulent diffusion simulations were performed on the carbon dioxide concentration and flue gas trajectory in the carbon flow data to generate a carbon diffusion mapping set. The digital twin model dynamically couples the coal delivery mapping set, steam energy mapping set, and carbon diffusion mapping set in real time to output a dynamic carbon flow topology map. The steps for performing reverse three-flow coupling feature analysis on the dynamic carbon flow topology map according to the coordinates of the halo device to generate device adjustment commands are as follows: The associated region of the halo device coordinates is located from the dynamic carbon flow topology map, and the transport change gradient, energy oscillation amplitude and diffusion turbulence intensity within the associated region are extracted and integrated to output the three-flow coupling feature dataset. Multimodal migration analysis is performed on the three-flow coupling feature dataset to detect the temporal mismatch between the gradient of the transport change and the amplitude of energy oscillation, and the feature migration index is output. The feature offset index is compared with the trend consistency of the carbon emission change slope, and an anomaly type identifier is output. The abnormality type identifier is matched with the preset equipment control strategy to generate equipment adjustment instructions.

2. The visualized carbon emission management method as described in claim 1, characterized in that: The specific steps involve spatially registering the dynamic carbon flow topology map with the coal-fired equipment of a coal-fired power plant, and statistically analyzing the slope of carbon emission changes for each coal-fired device at fixed time intervals. The dynamic carbon flow topology map is spatially aligned with the 3D image of the coal-fired equipment in the coal-fired power plant to generate a registration topology map. Carbon flux density data of each coal-fired device is extracted from the registration topology map, and the carbon flux density data is transformed by physical quantity mapping to output the carbon emission intensity sequence. Each carbon emission intensity sequence is subjected to a difference operation between adjacent points at fixed time intervals to generate the slope of carbon emission change.

3. The visualized carbon emission management method as described in claim 1, characterized in that: The specific steps for performing halo rendering on coal-fired equipment based on the slope of carbon emission changes and outputting a thermal rendering map are as follows: Determine the trend persistence of the slope of carbon emission changes; When the slope of carbon emission change is positive over a continuous period, coal-fired equipment is marked as having an upward trend in carbon emissions. When the slope of carbon emission change is negative over a continuous period, coal-fired equipment is marked as having a downward trend in carbon emissions. Based on the trend determination results, coal-fired equipment marked with an upward trend in carbon emissions is rendered with a gradually increasing halo effect, while coal-fired equipment marked with a downward trend in carbon emissions is rendered with a gradually decreasing halo effect, and a thermal rendering map is output.

4. The visualized carbon emission management method as described in claim 1, characterized in that: The specific steps for identifying the halo color of the coal-fired equipment in the thermal rendering map and performing correlation analysis with the carbon flow equipment, and outputting the coordinates of the halo equipment, are as follows: Extract pixel regions of gradually increasing and decreasing color halos from the thermal rendering image, map the pixel region coordinates of the pixel regions to the physical coordinate system of the coal-fired equipment, and determine the physical coordinate range of the halos. Geometric overlap detection is performed between the physical coordinate range of the halo and the spatial boundary of the coal-fired equipment to identify the identifiers of the coal-fired equipment that overlap. Verify the spatial correlation between the coal-fired equipment identifier and the carbon flow equipment in the dynamic carbon flow topology diagram, and output the verified coal-fired equipment identifier. Based on the verified coal-fired equipment identifier, the equipment coordinates are queried, and the coordinates of the halo equipment are output.

5. The visualized carbon emission management method as described in claim 1, characterized in that: The execution of the equipment adjustment command involves re-collecting carbon emission data and determining the effectiveness of the command. The specific steps are as follows: Execute equipment adjustment instructions to re-collect carbon emission data and output an updated carbon emission dataset; The thermal rendering map is updated based on the updated carbon emission dataset, and the updated thermal rendering map is output. Compare the intensity values ​​of the gradually increasing halo of the updated thermal rendering map with the intensity values ​​of the halo of the thermal rendering map at the same coordinates of the coal-fired equipment. If the intensity value of the halo of the updated thermal rendering map is lower than that of the thermal rendering map, then the equipment adjustment command is deemed to have been executed effectively.

6. The visualized carbon emission management method as described in claim 1, characterized in that: The process of overlaying the execution results, dynamic carbon flow topology map, and thermal rendering map onto a digital twin scene to form a visualized carbon emission management map involves the following steps: Spatially align the execution results, dynamic carbon flow topology map, and thermal rendering map in the digital twin scene, and output spatial alignment data; By visually overlaying the spatially aligned data, a visualized carbon emission management map is formed.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the visualized carbon emission management method according to any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the visualized carbon emission management method according to any one of claims 1 to 6.

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