Three-dimensional energy carbon data visualization terminal and use method thereof

Abstract

The invention discloses a three-dimensional energy carbon data visualization terminal and a use method thereof. The visualization terminal comprises a data acquisition module, a data processing module, a visualization display module, an interaction control module and a communication transmission module. The data acquisition module acquires a voltage signal, a current signal and a carbon emission signal of the park power equipment in real time; the data processing module performs energy-carbon coupling analysis on the original data to generate an analysis result; the visual display module dynamically displays the energy and carbon monitoring data, the electric power parameter waveform change trend, the incidence relation between energy consumption and carbon emission and abnormal event information; the interaction control module receives a user instruction and adjusts display content; and the communication transmission module performs data interaction with a remote management platform. Through multi-module cooperation, real-time acquisition, coupling analysis and three-dimensional dynamic visualization of energy and carbon data are realized, comprehensive energy efficiency and carbon efficiency management support is provided for an industrial park, and the decision efficiency of energy optimization and carbon emission regulation is improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a three-dimensional energy and carbon data visualization terminal and a method for using the same. Background Art

[0002] In the energy and carbon management systems of traditional industrial parks, data collection and analysis make it difficult to achieve real-time coupling analysis of voltage, current signals and carbon emission signals, and lack three-dimensional dynamic display and interactive control capabilities, resulting in delayed positioning of energy efficiency anomalies and difficulty in tracing carbon emissions. In addition, due to insufficient coordination between the communication transmission module and the data processing module, it is impossible to support the spatiotemporal alignment and deep correlation of multi-source heterogeneous data, which restricts the real-time and accuracy of park-level energy and carbon collaborative optimization decisions. Summary of the Invention

[0003] In view of this, the present invention proposes a three-dimensional energy and carbon data visualization terminal and its use method to solve the problems of insufficient real-time coupling analysis of energy and carbon data in industrial parks and lack of three-dimensional dynamic visualization and interactive control.

[0004] In order to achieve the above-mentioned technical objectives, in the first aspect, the present application provides a three-dimensional energy-carbon data visualization terminal, including a data acquisition module, a data processing module, a visualization display module, an interactive control module and a communication transmission module. The data acquisition module is used to collect raw data in real time, and the raw data includes the voltage signal, current signal and carbon emission signal of the park power equipment; the data processing module is connected to the data acquisition module, and is used to perform energy-carbon coupling analysis on the raw data to obtain analysis results; the visualization display module is connected to the data processing module, and is used to dynamically display the analysis results of the energy-carbon coupling analysis; the interactive control module is connected to the visualization display module, and is used to receive user instructions and adjust the display content; the communication transmission module is used to interact with the remote management platform for data.

[0005] In some embodiments, the analysis results include energy-carbon monitoring data, waveform change trends of power parameters, the correlation between energy consumption and carbon emissions, and abnormal event information. The visualization display module includes a main display unit, a waveform analysis unit, an energy-carbon mapping unit, and an event recording unit. The main display unit is used to display the main interface of real-time energy-carbon monitoring data; the waveform analysis unit is used to display the waveform change trends of power parameters; the energy-carbon mapping unit is used to establish and display the correlation between energy consumption and carbon emissions; and the event recording unit is used to store and display abnormal event information.

[0006] In some embodiments, the correlation between energy consumption and carbon emissions includes energy consumption data, energy utilization efficiency, and energy-carbon coupling relationship. The energy-carbon mapping unit includes a carbon emission calculation subunit, an energy efficiency evaluation subunit, and a multi-dimensional display subunit. The carbon emission calculation subunit is used to calculate carbon emissions based on energy consumption data; the energy efficiency evaluation subunit is used to analyze energy utilization efficiency; and the multi-dimensional display subunit is used to display the energy-carbon coupling relationship in the form of a chart.

[0007] In some embodiments, the data processing module includes a data preprocessing unit, a feature extraction unit, a coupling analysis unit and a storage management unit. The data preprocessing unit is used to filter and standardize the original data to obtain preprocessed data; the feature extraction unit is used to extract energy-carbon characteristic parameters from the preprocessed data; the coupling analysis unit is used to establish an energy-carbon coupling model based on the energy-carbon characteristic parameters to obtain analysis results; the storage management unit is used to classify and store the processed energy-carbon characteristic parameters and analysis results.

[0008] In a second aspect, the present invention further provides a method for using a three-dimensional energy and carbon data visualization terminal, which is applicable to the visualization terminal described in the first aspect, and the method comprises:

[0009] Real-time collection of raw data, including voltage and current signals of the park's power equipment, as well as carbon emission signals. The carbon emission signals are configured to be obtained through dynamic calculation of the electricity-to-carbon conversion factor.

[0010] Preprocess the original data and use the multi-source data spatiotemporal alignment algorithm to eliminate the time delay difference and obtain preprocessed data;

[0011] Perform feature extraction on pre-processed data and extract energy-carbon characteristic parameters through harmonic decomposition and load feature identification;

[0012] An energy-carbon coupling model is established based on energy-carbon characteristic parameters. A three-dimensional topological mapping algorithm is used to spatially correlate voltage signals, current signals, carbon emission signals, and equipment operating status to obtain analysis results. The analysis results include energy-carbon monitoring data, waveform change trends of power parameters, the correlation between energy consumption and carbon emissions, and abnormal event information.

[0013] The analysis results are presented, including:

[0014] Dynamically render the waveform change trend of power parameters in a three-dimensional coordinate system;

[0015] The relationship between energy consumption and carbon emissions of equipment in different areas is displayed through a heat map matrix;

[0016] Automatically store and hierarchically display abnormal event information based on event triggering mechanism.

[0017] In some embodiments, the carbon emission signal is configured to be obtained by dynamically calculating the electricity-to-carbon conversion factor, including:

[0018] Collect real-time operating parameters of the park's power equipment, including equipment type, load rate, and operating time;

[0019] Synchronously obtain power grid emission factors and environmental monitoring data;

[0020] Generate dynamic electricity-to-carbon conversion factors based on real-time operating parameters and grid emission factors;

[0021] The real-time power consumption is calculated based on the collected voltage and current signals and is expressed by formula (1). Formula (1) is as follows:

[0022]

[0023] In formula (1), E is the energy consumption, U(t) is the real-time voltage signal, and I(t) is the real-time current signal;

[0024] Match the dynamic electricity-to-carbon conversion factor of the current equipment and calculate the real-time carbon emission signal, which is expressed by formula (2). Formula (2) is as follows:

[0025] C=E·k+C0;

[0026] In formula (2), C is the real-time carbon emission signal, k is the dynamic electricity-carbon conversion factor, and C0 is the inherent carbon emission compensation value of the equipment;

[0027] Monitor the changes in load rate and fluctuations in grid emission factors in real-time operating parameters;

[0028] When the change in load rate exceeds the preset load threshold, or the fluctuation in the grid emission factor exceeds the preset emission threshold, the recalculation of the dynamic electricity-to-carbon conversion factor is triggered;

[0029] The dynamic electricity-to-carbon conversion factor is updated using a time-weighted average algorithm;

[0030] Comparing the real-time carbon emission signal with the actual measurement value of the carbon emission monitoring device;

[0031] When the comparison difference exceeds the preset error threshold, the inherent carbon emission compensation value of the equipment is adjusted;

[0032] Record the adjustment process of the dynamic electricity-to-carbon conversion factor and the equipment's inherent carbon emission compensation value in the conversion factor database.

[0033] In some embodiments, the raw data is preprocessed and a multi-source data spatiotemporal alignment algorithm is used to eliminate time delay differences. The preprocessed data obtained includes:

[0034] Establish a unified time base and synchronize the timestamps of voltage, current and carbon emission signals to the time coordinate system;

[0035] The voltage and current signals are aggregated using a weighted average method at fixed time intervals to ensure that the time resolution of the voltage and current signals matches that of the carbon emission signal.

[0036] Delay calibration testing is used to determine the inherent transmission delay of each signal acquisition terminal, including:

[0037] Measuring the first processing delay of the voltage signal acquisition terminal from signal input to data output;

[0038] Measuring the second processing delay of the current signal acquisition terminal from signal input to data output;

[0039] Measuring the third processing delay from signal input to data output of the carbon emission signal collection terminal;

[0040] Based on the network transmission delay monitoring results, the time compensation amount of each signal is dynamically adjusted, including:

[0041] Real-time monitoring of voltage signal transmission network delay fluctuations;

[0042] Real-time monitoring of current signal transmission network delay fluctuations;

[0043] Real-time monitoring of carbon emission signal transmission network delay fluctuations;

[0044] Establish a time delay compensation matrix to perform time calibration on each signal data at each acquisition moment, ensuring that the voltage, current, and carbon emission data collected at the same moment have a strict time alignment relationship;

[0045] Verify the validity of the time-aligned data, including:

[0046] Verify whether the phase relationship between the voltage signal and the current signal conforms to physical laws;

[0047] Verify whether the carbon emission signal matches the changing trend of electricity consumption;

[0048] Eliminate data points that do not meet the verification criteria;

[0049] The data that has been strictly time-aligned and validated are classified and stored according to device identification to form a preprocessed data set for subsequent analysis. The preprocessed data set includes multiple preprocessed data.

[0050] In some embodiments, feature extraction is performed on pre-processed data, and energy-carbon feature parameters are extracted through harmonic decomposition and load feature identification, including:

[0051] Perform fast Fourier transform on the voltage and current signals to obtain the current waveform characteristics, extract the fundamental wave and 2-65 harmonic components, calculate the amplitude, phase and harmonic distortion rate of each harmonic, and obtain the harmonic component parameters;

[0052] The load characteristic recognition algorithm is used to analyze the current waveform characteristics and extract the load characteristic parameters, including:

[0053] Calculate the peak-to-valley ratio and waveform factor of the current waveform;

[0054] Identify the mutation points and periodic characteristics of the current waveform;

[0055] Extract transient response characteristics during load switching;

[0056] Establish the correlation matrix between harmonic component parameters and load characteristic parameters, and map the harmonic characteristics to the load class type;

[0057] Calculate instantaneous carbon emission intensity based on carbon emission signals and conduct correlation analysis with current load characteristics;

[0058] Extract energy-carbon characteristic parameters, including:

[0059] Calculate carbon emissions per unit of electricity consumption;

[0060] Analyze carbon emission characteristics under different load types;

[0061] Establish the correlation between harmonic distortion rate and carbon emission efficiency;

[0062] Obtain energy-carbon characteristic parameters;

[0063] The extracted energy and carbon characteristic parameters are stored in time series.

[0064] In some embodiments, an energy-carbon coupling model is established based on energy-carbon characteristic parameters, and a three-dimensional topological mapping algorithm is used to spatially correlate voltage signals, current signals, carbon emission signals, and equipment operating status. The analysis results include:

[0065] Construct a three-dimensional energy-carbon space coordinate system, which includes a first dimension, a second dimension, and a third dimension. The first dimension corresponds to voltage characteristic parameters, which include the effective value of the fundamental voltage and the harmonic voltage content rate. The second dimension corresponds to current characteristic parameters, which include the effective value of the fundamental current and load characteristic parameters. The third dimension corresponds to carbon emission characteristic parameters, which include real-time carbon emissions and carbon emission intensity.

[0066] Synchronously mapping the pre-processed voltage signal, current signal and carbon emission signal characteristic parameters to a three-dimensional coordinate system to form an energy-carbon data point set, which includes multiple energy-carbon data points;

[0067] Perform spatial cluster analysis on the energy and carbon data point set, including:

[0068] The spatial distance between multiple energy-carbon data points is calculated using the Euclidean distance metric;

[0069] The density clustering algorithm is used to identify the cluster of energy and carbon data points under normal working conditions, which is recorded as the normal cluster;

[0070] Marking outlier data points that deviate from the normal cluster;

[0071] Establish an energy-carbon coupling relationship model, including:

[0072] Calculate the correlation coefficient matrix between the three parameters of voltage, current and carbon emissions;

[0073] Construct an energy-carbon coupling strength evaluation function;

[0074] Determine the boundary conditions of the optimal energy efficiency operating range;

[0075] Output analysis result data, including:

[0076] Generate a three-dimensional spatial distribution map of energy and carbon;

[0077] Calculate the degree of deviation of each abnormal data point;

[0078] Provides the parameter range for energy-carbon coupling optimization;

[0079] Output an evaluation report on the energy efficiency of each device.

[0080] In some embodiments, dynamically rendering the waveform change trend of the power parameter in the three-dimensional coordinate system includes:

[0081] Establish a time-amplitude-phase three-dimensional waveform space, which includes a time axis, an amplitude axis, and a phase axis. The time axis represents the signal acquisition time series, the amplitude axis represents the instantaneous amplitude of the voltage or current signal, and the phase axis represents the signal phase angle change;

[0082] Map the waveform data of continuously collected voltage and current signals into three-dimensional space in real time;

[0083] Use dynamic interpolation algorithm to generate smooth three-dimensional waveform surface;

[0084] Automatically adjust the 3D viewing angle and rendering frequency according to the characteristic change rate of the current signal and voltage signal;

[0085] The heat map matrix shows the relationship between energy consumption and carbon emissions of equipment in different regions, including:

[0086] Construct a two-dimensional heat map coordinate system. The two-dimensional heat map coordinate system has a horizontal axis and a vertical axis. The horizontal axis represents the regional distribution of equipment, and the vertical axis represents the time segment interval.

[0087] Calculate the energy-carbon correlation intensity coefficient of each device in the corresponding period;

[0088] The energy-carbon correlation intensity coefficient is visualized using a color gradient mapping algorithm;

[0089] Set dynamic thresholds to trigger regional energy and carbon anomaly warning signs;

[0090] Automatic storage and hierarchical display of abnormal event information based on event triggering mechanism include:

[0091] Set multi-level event trigger threshold conditions, including voltage swell / sag event trigger thresholds, harmonic excess event trigger thresholds, and energy-carbon coupling imbalance event trigger thresholds;

[0092] Establish an event hierarchical storage strategy. The event classification includes level 1 events, level 2 events, level 3 events. Level 1 events store complete waveform data in real time, level 2 events store characteristic parameter snapshots, and level 3 events only record event logs.

[0093] According to the event classification display scheme, the first-level event triggers a full-screen alarm display, the second-level event is prompted in the dedicated alarm area, and the third-level event generates a statistical report entry.

[0094] By adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art:

[0095] The above technical solution provides a three-dimensional energy and carbon data visualization terminal and its use method. The visualization terminal includes a data acquisition module, a data processing module, a visualization display module, an interactive control module, and a communication transmission module. The data acquisition module collects the voltage signal, current signal, and carbon emission signal of the park's power equipment in real time; the data processing module performs energy-carbon coupling analysis on the original data and generates analysis results; the visualization display module dynamically displays energy and carbon monitoring data, power parameter waveform change trends, the correlation between energy consumption and carbon emissions, and abnormal event information; the interactive control module receives user instructions and adjusts the display content; and the communication transmission module interacts with the remote management platform for data. The above technical solution realizes the real-time acquisition, coupling analysis, and three-dimensional dynamic visualization of energy and carbon data through the collaboration of multiple modules, providing comprehensive energy efficiency and carbon efficiency management support for industrial parks and improving the decision-making efficiency of energy optimization and carbon emission regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0097] Figure 1 2. It is a structural diagram of the three-dimensional energy carbon data visualization terminal described in the specific implementation method;

[0098] Figure 2 It is a method step diagram of steps S101 to S105 of the method for using the three-dimensional energy carbon data visualization terminal described in the specific implementation method.

[0099] The reference numerals are as follows:

[0100] 1. Visual terminal;

[0101] 11. Data acquisition module;

[0102] 12. Data processing module;

[0103] 13. Visual display module;

[0104] 14. Interactive control module;

[0105] 15. Communication transmission module. DETAILED DESCRIPTION

[0106] The present invention will be described in further detail below with reference to the accompanying drawings and examples. It is particularly noted that the following examples are intended only to illustrate the present invention and are not intended to limit the scope of the present invention. Similarly, the following examples are only some embodiments of the present invention and are not intended to be exhaustive. All other embodiments obtained by those of ordinary skill in the art without creative effort are intended to fall within the scope of protection of the present invention.

[0107] See also Figure 1In the first aspect, this embodiment provides a three-dimensional energy-carbon data visualization terminal 1, including a data acquisition module 11, a data processing module 12, a visualization display module 13, an interactive control module 14 and a communication transmission module 15. The data acquisition module 11 is used to collect raw data in real time, and the raw data includes voltage signals, current signals and carbon emission signals of the park power equipment; the data processing module 12 is connected to the data acquisition module 11, and is used to perform energy-carbon coupling analysis on the raw data to obtain analysis results; the visualization display module 13 is connected to the data processing module 12, and is used to dynamically display the analysis results of the energy-carbon coupling analysis; the interactive control module 14 is connected to the visualization display module 13, and is used to receive user instructions and adjust the display content; the communication transmission module 15 is used to interact with the remote management platform for data.

[0108] In this embodiment, the three-dimensional energy and carbon data visualization terminal 1 uses data acquisition module 11 to collect real-time voltage, current, and carbon emission signals from the park's power equipment. The voltage and current signals are directly derived from the power equipment's operation monitoring devices, while the carbon emission signal is generated by dynamically calculating the electricity-to-carbon conversion factor or directly measured by a dedicated carbon emission sensor. Preferably, data acquisition module 11 utilizes multi-channel synchronous acquisition technology to cover the operating parameters of key equipment within the park, such as distributed photovoltaic units and energy storage power stations, ensuring the comprehensiveness and real-time nature of the raw data.

[0109] Data processing module 12 is connected to data acquisition module 11. Its core function is to perform energy-carbon coupling analysis on raw data, including filtering to eliminate noise interference, standardizing the data format, and extracting features to identify energy-carbon correlations. Ultimately, it generates analysis results using a coupled model. Data processing module 12 integrates high-dimensional spatiotemporal data space technology, supports the fusion of multi-source heterogeneous data, and provides structured data support for subsequent visualization.

[0110] The visualization display module 13 converts the analysis results into an intuitive visualization interface based on three-dimensional dynamic rendering technology. The dynamic display content includes the real-time values ​​of energy-carbon monitoring data, the spatiotemporal variation trends of voltage and current waveforms, the correlation topology of energy consumption and carbon emissions, and the spatiotemporal distribution of abnormal events.

[0111] The interactive control module 14 is embedded with human-computer interaction logic, allowing users to adjust the display content through touch operations or external commands, such as switching three-dimensional viewing angles, zooming in and out of energy-carbon correlation maps, or retrieving historical event records, to achieve personalized configuration of data display.

[0112] Preferably, the communication transmission module 15 adopts a dual Ethernet redundant design, supports data interaction with the remote management platform, and realizes real-time data upload, control instruction issuance and system status synchronization through standardized protocols, ensuring information exchange and collaborative management between the terminal and the platform.

[0113] The visualization terminal 1 provided in this embodiment converts discrete energy-carbon data into multidimensional dynamic visualization information. Through energy-carbon coupling analysis, it reveals the deep connection between the operating status of power equipment and carbon emissions, helping managers quickly locate energy efficiency bottlenecks and carbon emission anomalies. The multi-source synchronization capability of the data acquisition module 11 ensures the accuracy of the analytical foundation. The coupled model of the data processing module 12 enables intelligent parsing of complex data. The visualization and interaction module lowers the threshold for understanding professional data. The communication transmission module 15 further expands the terminal's remote control capabilities. Through software and hardware collaborative design, this embodiment provides real-time perception and decision-making support tools for the coordinated optimization of energy and carbon in industrial parks.

[0114] In some embodiments, the analysis results include energy-carbon monitoring data, waveform change trends of power parameters, the correlation between energy consumption and carbon emissions, and abnormal event information. The visualization display module 13 includes a main display unit, a waveform analysis unit, an energy-carbon mapping unit, and an event recording unit. The main display unit is used to display the main interface of real-time energy-carbon monitoring data; the waveform analysis unit is used to display the waveform change trend of power parameters; the energy-carbon mapping unit is used to establish and display the correlation between energy consumption and carbon emissions; and the event recording unit is used to store and display abnormal event information.

[0115] In this embodiment, the main display unit serves as the core interface of the visual display module 13, integrating the dynamic display function of real-time energy and carbon monitoring data, including the numerical display of key parameters such as voltage effective value, current harmonic distortion rate, and instantaneous carbon emission intensity, and can present data change trends in a graphical manner through a color LCD screen.

[0116] The waveform analysis unit is based on high-resolution sampling technology to capture the continuous waveforms of voltage and current signals. Preferably, it displays the waveform change trend of power parameters through a three-dimensional space-time coordinate system, supporting users to observe the dynamic process of voltage swell / sag events, harmonic exceeding the standard phenomenon and load switching transient response, providing an intuitive basis for fault diagnosis.

[0117] The energy-carbon mapping unit constructs a correlation model between energy consumption and carbon emissions, conducting a multi-dimensional correlation analysis between electricity consumption, equipment operating efficiency, and carbon emissions. It optimally displays the energy-carbon coupling relationship between equipment in different regions using heat maps and topological diagrams. For example, it uses color gradients to identify high-carbon emission areas, or uses connection topologies to reflect energy flow paths and carbon emission distribution. The event recording unit, incorporating a threshold trigger mechanism, automatically stores information on abnormal events such as voltage limit violations, harmonic excesses, and energy-carbon imbalances. This log contains event type, occurrence time, and impact range, and displays it hierarchically in a visual interface, allowing users to review historical event details or retrieve complete waveform data before and after the event is triggered.

[0118] This embodiment achieves hierarchical presentation of specialized data through modular functional division. Specifically, the main display unit provides a global monitoring perspective, the waveform analysis unit focuses on power quality details, the energy-carbon mapping unit reveals the deep connection between energy and carbon emissions, and the event recording unit strengthens anomaly management capabilities. These units work together to meet real-time monitoring needs while supporting in-depth analytical decision-making. This helps users quickly identify energy efficiency anomalies, optimize load distribution strategies, and develop precise carbon management plans, ultimately improving the coordinated energy-carbon management level of industrial parks.

[0119] In some embodiments, the correlation between energy consumption and carbon emissions includes energy consumption data, energy utilization efficiency, and energy-carbon coupling relationship. The energy-carbon mapping unit includes a carbon emission calculation subunit, an energy efficiency evaluation subunit, and a multi-dimensional display subunit. The carbon emission calculation subunit is used to calculate carbon emissions based on energy consumption data; the energy efficiency evaluation subunit is used to analyze energy utilization efficiency; and the multi-dimensional display subunit is used to display the energy-carbon coupling relationship in the form of a chart.

[0120] In this embodiment, preferably, the carbon emission calculation subunit calculates the carbon emissions in real time by receiving energy consumption data, combining the dynamic electricity-to-carbon conversion factor and the preset equipment-inherent carbon emission compensation value, wherein the energy consumption data is derived from the integral operation result of the voltage signal and the current signal, and the dynamic electricity-to-carbon conversion factor is dynamically adjusted based on the grid emission factor, the equipment load rate and the environmental parameters to ensure that the generation process of the carbon emission signal reflects both the real-time electricity consumption characteristics and the inherent carbon emission characteristics of the equipment.

[0121] Preferably, the energy efficiency evaluation subunit analyzes energy utilization efficiency, associates electric energy consumption with equipment output power, and evaluates energy conversion efficiency under different operating modes in combination with load feature identification results. For example, it distinguishes high-efficiency equipment from low-efficiency equipment through cluster analysis and marks abnormal energy efficiency fluctuation events.

[0122] Preferably, the multi-dimensional display sub-unit integrates energy consumption data, carbon emissions and energy efficiency evaluation results into a three-dimensional topological map based on the energy-carbon coupling relationship model, displays the regional carbon emission intensity distribution through a dynamic heat map, reflects the energy efficiency change trend in the time dimension through a line graph, and reveals the potential connection between harmonic distortion rate and carbon emission efficiency through a correlation matrix.

[0123] The workflow of this embodiment can be understood as follows: extracting energy consumption data from preprocessed data and inputting it into the carbon emission calculation subunit to generate a carbon emission signal; simultaneously inputting energy consumption data and equipment operating parameters into the energy efficiency evaluation subunit for efficiency analysis; and finally, mapping the calculation results and associated relationships to a visual interface through the multi-dimensional display subunit. The carbon emission calculation subunit dynamically adjusts the electricity-to-carbon conversion factor, solving the problem that traditional static factors are unable to adapt to grid emission fluctuations and changes in equipment operating conditions; the energy efficiency evaluation subunit achieves refined classification of equipment energy efficiency status through load feature clustering; and the multi-dimensional display subunit uses multi-chart linkage technology to transform complex energy-carbon data into interactive visual elements, reducing the complexity of data analysis.

[0124] This embodiment builds a complete energy-carbon analysis chain from data calculation to visual presentation. The dynamic compensation mechanism of the carbon emission calculation subunit improves the accuracy of the carbon emission signal, the cluster analysis method of the energy efficiency evaluation subunit enhances the pertinence of the energy efficiency evaluation, and the multimodal presentation method of the multidimensional display subunit enhances the data insight capability. The three work together to provide a basis for carbon emission traceability and assist in formulating differentiated energy efficiency optimization strategies. At the same time, it lowers the decision-making threshold through intuitive visual interaction, and ultimately promotes the transformation of energy-carbon management in industrial parks from extensive monitoring to precise regulation.

[0125] In some embodiments, the data processing module 12 includes a data preprocessing unit, a feature extraction unit, a coupling analysis unit and a storage management unit. The data preprocessing unit is used to filter and standardize the original data to obtain preprocessed data; the feature extraction unit is used to extract energy-carbon characteristic parameters from the preprocessed data; the coupling analysis unit is used to establish an energy-carbon coupling model based on the energy-carbon characteristic parameters to obtain analysis results; the storage management unit is used to classify and store the processed energy-carbon characteristic parameters and analysis results.

[0126] In this embodiment, the data preprocessing unit eliminates noise interference in the original data through filtering, such as high-frequency electromagnetic interference signals or sensor acquisition errors, and uses standardization processing to unify the dimensions and sampling frequencies of voltage signals, current signals and carbon emission signals to generate time-aligned and consistent format preprocessed data.

[0127] The feature extraction unit processes the preprocessed data based on the harmonic decomposition algorithm, extracts energy-carbon characteristic parameters such as the effective value of the fundamental voltage, harmonic distortion rate, and load peak-to-valley ratio, and distinguishes the equipment operation mode through the load feature recognition algorithm, such as identifying the output fluctuation characteristics of distributed photovoltaic units or the charging and discharging transient characteristics of energy storage power stations.

[0128] The coupling analysis unit establishes an energy-carbon coupling model to correlate the extracted energy-carbon characteristic parameters with the equipment operating status, such as calculating the impact coefficient of voltage fluctuations on carbon emission efficiency or analyzing the mapping relationship between load type and carbon emission intensity, and finally generates analysis results including energy-carbon correlation intensity, abnormal deviation and optimization suggestions.

[0129] The storage management unit adopts a hierarchical storage architecture, storing pre-processed data in a high-speed cache area according to device identification, archiving energy carbon characteristic parameters and analysis results in a time series to a distributed database, and establishing an indexing mechanism to support fast retrieval and historical data backtracking.

[0130] The workflow of this embodiment can be understood as follows: after the data preprocessing unit cleans and converts the raw data, the feature extraction unit mines key energy-carbon characteristic parameters. The coupled analysis unit constructs a dynamic correlation model based on these characteristic parameters and outputs analysis conclusions. The storage management unit simultaneously completes data archiving and classification management. The filtering and standardization operations of the data preprocessing unit improve data quality, laying a solid foundation for subsequent analysis; the feature extraction unit's multi-dimensional parameter extraction capability enhances the granularity of characterizing equipment operating status; the coupled analysis unit's model-driven approach enables quantitative analysis of energy-carbon relationships; and the storage management unit's layered design balances real-time performance with storage efficiency.

[0131] This embodiment establishes a full-link processing capability from data cleaning to intelligent analysis. The data preprocessing unit ensures the accuracy of the analysis input, the feature extraction unit strengthens the data characterization capability, the coupling analysis unit reveals the energy-carbon coupling law through modeling methods, and the storage management unit ensures the availability and traceability of data assets. The four work together to form a closed-loop processing chain, which not only improves the efficiency of analyzing complex energy-carbon data, but also provides a high-confidence decision-making basis for the visualization terminal 1, ultimately supporting the industrial park to achieve full-process energy-carbon collaborative management from data collection to optimization and regulation.

[0132] See also Figure 2 In a second aspect, this embodiment further provides a method for using a three-dimensional energy and carbon data visualization terminal, which is applicable to the visualization terminal described in the first aspect, and the method includes:

[0133] S101. Real-time collection of raw data, including voltage signals, current signals, and carbon emission signals of the park's power equipment. The carbon emission signals are configured to be obtained through dynamic calculation of the electricity-to-carbon conversion factor.

[0134] S102, preprocessing the original data, using a multi-source data spatiotemporal alignment algorithm to eliminate delay differences, and obtaining preprocessed data;

[0135] S103, extracting features from the pre-processed data, and extracting energy-carbon feature parameters through harmonic decomposition and load feature identification;

[0136] S104. Establish an energy-carbon coupling model based on the energy-carbon characteristic parameters, and use a three-dimensional topological mapping algorithm to spatially correlate voltage signals, current signals, carbon emission signals, and equipment operating status to obtain analysis results. The analysis results include energy-carbon monitoring data, waveform change trends of power parameters, correlations between energy consumption and carbon emissions, and abnormal event information.

[0137] S105. Display the analysis results, including:

[0138] Dynamically render the waveform change trend of power parameters in a three-dimensional coordinate system;

[0139] The relationship between energy consumption and carbon emissions of equipment in different areas is displayed through a heat map matrix;

[0140] Automatically store and hierarchically display abnormal event information based on event triggering mechanism.

[0141] In step S101, the data acquisition module of the aforementioned embodiment is used to collect voltage signals, current signals and carbon emission signals of the park power equipment in real time, wherein the voltage signal and current signal are directly derived from the synchronous sampling results of the power monitoring device, and the carbon emission signal is generated by calculating the dynamic electricity-carbon conversion factor. The dynamic electricity-carbon conversion factor is updated in real time in combination with the grid emission factor, equipment load rate and operating environment parameters to ensure that the generation process of the carbon emission signal can reflect the actual operating status of the equipment and changes in the external environment.

[0142] In step S102, during the preprocessing phase, a multi-source data spatiotemporal alignment algorithm is used to synchronize timestamps and unify the format of the raw data. Preferably, by measuring the inherent transmission delay of each signal acquisition terminal and dynamically compensating for delay differences caused by network fluctuations, the time misalignment between voltage, current, and carbon emission signals is eliminated, ensuring that the multi-source data collected at the same moment is strictly time-aligned. Furthermore, the time-aligned data can be further validated to eliminate invalid data with abnormal phase relationships or mismatched energy and carbon trends, forming a preprocessed dataset for subsequent analysis.

[0143] In step S103, the feature extraction phase performs harmonic decomposition and load signature identification on the preprocessed data. Preferably, a fast Fourier transform is used to extract the fundamental and 2-65th harmonic components of the voltage and current signals. Harmonic distortion and waveform characteristic parameters are calculated. The transient response characteristics of load switching are combined to identify the equipment operating mode, and the harmonic characteristics are mapped and associated with the load type. Furthermore, the instantaneous carbon emission intensity is calculated based on the carbon emission signal, and its dynamic correlation with the load characteristic parameters is analyzed. Ultimately, energy-carbon characteristic parameters such as carbon emissions per unit of energy consumption and the energy-carbon efficiency coupling coefficient are extracted.

[0144] In step S104, an energy-carbon coupling model is established based on the energy-carbon characteristic parameters. A three-dimensional topological mapping algorithm is used to map the voltage characteristic parameters, current characteristic parameters, and carbon emission characteristic parameters to a three-dimensional spatial coordinate system, forming an energy-carbon data point set. Preferably, spatial clustering analysis is performed to identify normal operating condition clusters and abnormal deviation points, construct an energy-carbon coupling strength assessment function, and calculate the quantitative correlation between voltage fluctuations, harmonic distortion, and carbon emission efficiency. This generates analysis results including a three-dimensional spatial distribution map, abnormal deviation, and optimized parameter range.

[0145] In step S105, the results display phase dynamically renders the analysis results to a visualization interface, displaying the voltage and current waveform trends in a three-dimensional time-amplitude-phase coordinate system. A heat map matrix identifies the energy-carbon correlation strength of equipment in different regions. Based on an event-triggered mechanism, abnormal event information is automatically stored and displayed in a hierarchical manner. Preferably, a Level 1 event triggers a full-screen alarm and stores the complete waveform data; a Level 2 event displays a snapshot of key parameters in a dedicated alarm area; and a Level 3 event generates statistical report entries for historical review.

[0146] This embodiment achieves in-depth analysis and dynamic visualization of energy-carbon data through full-process data synchronization, feature extraction, and spatial association. It not only improves the response speed to abnormal events, but also strengthens the quantitative analysis capabilities of energy-carbon coupling relationships, providing an operational technical path for refined energy efficiency management and carbon emission control in industrial parks.

[0147] In some embodiments, the carbon emission signal is configured to be obtained by dynamically calculating the electricity-to-carbon conversion factor, including:

[0148] Collect real-time operating parameters of the park's power equipment, including equipment type, load rate, and operating time;

[0149] Synchronously obtain power grid emission factors and environmental monitoring data;

[0150] Generate dynamic electricity-to-carbon conversion factors based on real-time operating parameters and grid emission factors;

[0151] The real-time power consumption is calculated based on the collected voltage and current signals and is expressed by formula (1). Formula (1) is as follows:

[0152]

[0153] In formula (1), E is the energy consumption, U(t) is the real-time voltage signal, and I(t) is the real-time current signal;

[0154] Match the dynamic electricity-to-carbon conversion factor of the current equipment and calculate the real-time carbon emission signal, which is expressed by formula (2). Formula (2) is as follows:

[0155] C=E·k+C0;

[0156] In formula (2), C is the real-time carbon emission signal, k is the dynamic electricity-carbon conversion factor, and C0 is the inherent carbon emission compensation value of the equipment;

[0157] Monitor the changes in load rate and fluctuations in grid emission factors in real-time operating parameters;

[0158] When the change in load rate exceeds the preset load threshold, or the fluctuation in the grid emission factor exceeds the preset emission threshold, the recalculation of the dynamic electricity-to-carbon conversion factor is triggered;

[0159] The dynamic electricity-to-carbon conversion factor is updated using a time-weighted average algorithm;

[0160] Comparing the real-time carbon emission signal with the actual measurement value of the carbon emission monitoring device;

[0161] When the comparison difference exceeds the preset error threshold, the inherent carbon emission compensation value of the equipment is adjusted;

[0162] Record the adjustment process of the dynamic electricity-to-carbon conversion factor and the equipment's inherent carbon emission compensation value in the conversion factor database.

[0163] In this embodiment, the real-time electric energy consumption is calculated by integrating the real-time voltage signal U(t) and the real-time current signal I(t), the electric energy consumption E is multiplied by the dynamic electricity-to-carbon conversion factor, and the equipment-inherent carbon emission compensation value C0 is superimposed to obtain the real-time carbon emission signal C. Preferably, the equipment-inherent carbon emission compensation value C0 is pre-set based on the equipment manufacturing material, aging degree and maintenance record.

[0164] When the load rate change exceeds the preset load threshold, or the fluctuation of the grid emission factor exceeds the preset emission threshold, the dynamic electricity-to-carbon conversion factor is recalculated. A time-weighted average algorithm is used to integrate historical factors and current environmental parameters, and the dynamic electricity-to-carbon conversion factor is updated to reflect real-time operating condition changes. The preset load threshold is based on the load rate distribution statistics of the equipment's historical operating data, combined with the equipment's rated parameters and the manufacturer's recommended safety margin. For example, 85% of the historical peak load is used as the dynamic adjustment trigger line. The preset emission threshold is determined based on the annual fluctuation range of the grid emission factor and the environmental protection standard limit. It is usually set to ±10% of the average emission factor of the grid area to reflect reasonable fluctuations.

[0165] The generated real-time carbon emission signal is compared with the actual measurement value of the carbon emission monitoring device. If the comparison difference exceeds the preset error threshold, the device's inherent carbon emission compensation value is adjusted based on the deviation direction to ensure consistency between the calculated model and the actual measurement value. The preset error threshold is set based on the measurement accuracy level of the carbon emission monitoring device, for example, matching 1.5 times the nominal error range of the sensor to ensure that the deviation between the calculated value and the measured value is within an acceptable tolerance.

[0166] The dynamic electricity-to-carbon conversion factor and the adjusted equipment-inherent carbon emission compensation value are recorded in the conversion factor database, forming a complete log with timestamp, adjustment reason and correction parameters, supporting historical traceability and model optimization.

[0167] This embodiment solves the problem that traditional static models are unable to adapt to grid fluctuations and equipment aging through a dynamic factor generation and compensation value calibration mechanism. It not only improves the accuracy of carbon emission signals, but also enhances the adaptability of the calculation model, providing a reliable data basis for carbon emission accounting and energy efficiency optimization.

[0168] In some embodiments, the raw data is preprocessed and a multi-source data spatiotemporal alignment algorithm is used to eliminate time delay differences. The preprocessed data obtained includes:

[0169] Establish a unified time base and synchronize the timestamps of voltage, current and carbon emission signals to the time coordinate system;

[0170] The voltage and current signals are aggregated using a weighted average method at fixed time intervals to ensure that the time resolution of the voltage and current signals matches that of the carbon emission signal.

[0171] Delay calibration testing is used to determine the inherent transmission delay of each signal acquisition terminal, including:

[0172] Measuring the first processing delay of the voltage signal acquisition terminal from signal input to data output;

[0173] Measuring the second processing delay of the current signal acquisition terminal from signal input to data output;

[0174] Measuring the third processing delay from signal input to data output of the carbon emission signal collection terminal;

[0175] Based on the network transmission delay monitoring results, the time compensation amount of each signal is dynamically adjusted, including:

[0176] Real-time monitoring of voltage signal transmission network delay fluctuations;

[0177] Real-time monitoring of current signal transmission network delay fluctuations;

[0178] Real-time monitoring of carbon emission signal transmission network delay fluctuations;

[0179] Establish a time delay compensation matrix to perform time calibration on each signal data at each acquisition moment, ensuring that the voltage, current, and carbon emission data collected at the same moment have a strict time alignment relationship;

[0180] Verify the validity of the time-aligned data, including:

[0181] Verify whether the phase relationship between the voltage signal and the current signal conforms to physical laws;

[0182] Verify whether the carbon emission signal matches the changing trend of electricity consumption;

[0183] Eliminate data points that do not meet the verification criteria;

[0184] The data that has been strictly time-aligned and validated are classified and stored according to device identification to form a preprocessed data set for subsequent analysis. The preprocessed data set includes multiple preprocessed data.

[0185] In this embodiment, the process of eliminating delay differences by the multi-source data spatiotemporal alignment algorithm can be understood as: establishing a unified time base, synchronizing the timestamps of the voltage signal, current signal and carbon emission signal to the same time coordinate system, and preferably, ensuring the clock synchronization of each acquisition terminal through a timing server or B code timing signal.

[0186] The voltage and current signals are aggregated using a weighted average method with a fixed time interval, and the sampling frequency is adjusted to be consistent with the time resolution of the carbon emission signal to eliminate data misalignment caused by differences in acquisition rates.

[0187] The inherent transmission delay is determined through delay calibration testing. The first processing delay from signal input to data output of the voltage signal acquisition terminal, the second processing delay of the current signal acquisition terminal, and the third processing delay of the carbon emission signal acquisition terminal are measured to form an inherent delay parameter table for each terminal.

[0188] Preferably, the delay compensation matrix measures the first processing delay, the second processing delay, and the third processing delay, and combines the real-time monitored voltage signal transmission network delay fluctuations, current signal transmission network delay fluctuations, and carbon emission signal transmission network delay fluctuations to construct a time offset comparison table according to the device and signal type, and dynamically superimposes and calibrates the signal acquisition moments, so that the voltage, current, and carbon emission data collected at the same moment are strictly aligned.

[0189] Furthermore, the validity of the time-aligned data is verified to verify whether the phase relationship between the voltage signal and the current signal conforms to physical laws such as Kirchhoff's law, and whether the changing trends of the carbon emission signal and the power consumption match the equipment operation logic, and invalid data points with excessive phase deviation or inconsistent energy-carbon trends are eliminated.

[0190] The calibrated and verified data are stored according to the device identification to form a pre-processed data set containing timestamp, device type and data validity flag.

[0191] This embodiment solves the problem of timing inaccuracy in multi-source data caused by differences in collection terminals and network fluctuations through precise time delay calibration and dynamic compensation mechanisms, ensuring that the data basis for energy-carbon coupling analysis has strict time consistency, providing high-confidence input data for subsequent feature extraction and model building, and filtering out abnormal data through validity verification, thereby improving the reliability of the overall analysis results and the decision-making support value.

[0192] In some embodiments, feature extraction is performed on pre-processed data, and energy-carbon feature parameters are extracted through harmonic decomposition and load feature identification, including:

[0193] Perform fast Fourier transform on the voltage and current signals to obtain the current waveform characteristics, extract the fundamental wave and 2-65 harmonic components, calculate the amplitude, phase and harmonic distortion rate of each harmonic, and obtain the harmonic component parameters;

[0194] The load characteristic recognition algorithm is used to analyze the current waveform characteristics and extract the load characteristic parameters, including:

[0195] Calculate the peak-to-valley ratio and waveform factor of the current waveform;

[0196] Identify the mutation points and periodic characteristics of the current waveform;

[0197] Extract transient response characteristics during load switching;

[0198] Establish the correlation matrix between harmonic component parameters and load characteristic parameters, and map the harmonic characteristics to the load class type;

[0199] Calculate instantaneous carbon emission intensity based on carbon emission signals and conduct correlation analysis with current load characteristics;

[0200] Extract energy-carbon characteristic parameters, including:

[0201] Calculate carbon emissions per unit of electricity consumption;

[0202] Analyze carbon emission characteristics under different load types;

[0203] Establish the correlation between harmonic distortion rate and carbon emission efficiency;

[0204] Obtain energy-carbon characteristic parameters;

[0205] The extracted energy and carbon characteristic parameters are stored in time series.

[0206] In this embodiment, the process of feature extraction of preprocessed data can be understood as: converting the voltage time domain signal and the current time domain signal into the frequency domain through fast Fourier transform, locating the fundamental frequency (such as 50Hz) and its 2-65 times harmonic components, decomposing the real and imaginary parts of each harmonic, calculating the amplitude (root sum of the real and imaginary squares) and phase (inverse tangent of the imaginary-real ratio), and the total harmonic distortion rate is determined by the percentage of the root sum of the squares of the harmonic amplitudes to the fundamental amplitude, and finally generating a harmonic component parameter set including amplitude, phase and distortion rate.

[0207] By analyzing the correlation between current waveforms and carbon emissions, a quantitative relationship between equipment operating status and carbon emission efficiency is established. Specifically, by calculating the peak-to-valley ratio and waveform coefficient of the current waveform and identifying sudden changes, equipment load characteristics (such as motor start-stop and photovoltaic output fluctuations) can be determined and load types can be distinguished. Extracting the transient response of load switching can also help locate abnormal operating conditions.

[0208] By associating harmonic component parameters (such as the third harmonic amplitude) with load types, the types of high harmonic pollution equipment can be identified, providing a basis for governance.

[0209] Based on carbon emission signals, the system calculates carbon emissions per unit of electricity consumption, analyzes carbon efficiency differences based on load type (e.g., whether high-load equipment is associated with low energy efficiency), and establishes a negative correlation between harmonic distortion and carbon efficiency (e.g., harmonics increase losses and reduce carbon efficiency). Finally, energy-carbon characteristic parameters are stored in a time series to form a historical database, supporting carbon emission traceability, energy efficiency optimization, and dynamic adjustment of harmonic control strategies, achieving refined energy-carbon management.

[0210] Based on the carbon emission signal, instantaneous carbon emission intensity is calculated. Combined with the current load characteristics, the carbon emission efficiency differences of different equipment types are analyzed (e.g., whether high-load equipment is associated with low energy efficiency). A negative correlation is established between harmonic distortion rate and carbon emission efficiency (e.g., increased losses due to harmonics decrease carbon emission efficiency). Energy-carbon characteristic parameters, such as carbon emissions per unit of electrical energy consumption and the energy-carbon efficiency coupling coefficient, are extracted. The relationship between harmonic distortion rate and carbon emission efficiency is quantified as characteristic parameters. The extracted energy-carbon characteristic parameters are stored in a database in time series, forming a structured dataset suitable for modeling and analysis.

[0211] This embodiment achieves a refined characterization of the operating status and carbon emission efficiency of power equipment through multi-dimensional analysis of harmonic decomposition and load characteristics. It not only provides high-precision input parameters for the energy-carbon coupling model, but also reveals the potential correlation between harmonic pollution and carbon efficiency loss through the correlation matrix of harmonic component parameters and load characteristic parameters. It is conducive to formulating a coordinated optimization strategy for harmonic governance and carbon emissions, and improving the comprehensive decision-making ability of energy-carbon management.

[0212] In some embodiments, an energy-carbon coupling model is established based on energy-carbon characteristic parameters, and a three-dimensional topological mapping algorithm is used to spatially correlate voltage signals, current signals, carbon emission signals, and equipment operating status. The analysis results include:

[0213] Construct a three-dimensional energy-carbon space coordinate system, which includes a first dimension, a second dimension, and a third dimension. The first dimension corresponds to voltage characteristic parameters, which include the effective value of the fundamental voltage and the harmonic voltage content rate. The second dimension corresponds to current characteristic parameters, which include the effective value of the fundamental current and load characteristic parameters. The third dimension corresponds to carbon emission characteristic parameters, which include real-time carbon emissions and carbon emission intensity.

[0214] Synchronously mapping the pre-processed voltage signal, current signal and carbon emission signal characteristic parameters to a three-dimensional coordinate system to form an energy-carbon data point set, which includes multiple energy-carbon data points;

[0215] Perform spatial cluster analysis on the energy and carbon data point set, including:

[0216] The spatial distance between multiple energy-carbon data points is calculated using the Euclidean distance metric;

[0217] The density clustering algorithm is used to identify the cluster of energy and carbon data points under normal working conditions, which is recorded as the normal cluster;

[0218] Marking outlier data points that deviate from the normal cluster;

[0219] Establish an energy-carbon coupling relationship model, including:

[0220] Calculate the correlation coefficient matrix between the three parameters of voltage, current and carbon emissions;

[0221] Construct an energy-carbon coupling strength evaluation function;

[0222] Determine the boundary conditions of the optimal energy efficiency operating range;

[0223] Output analysis result data, including:

[0224] Generate a three-dimensional spatial distribution map of energy and carbon;

[0225] Calculate the degree of deviation of each abnormal data point;

[0226] Provides the parameter range for energy-carbon coupling optimization;

[0227] Output an evaluation report on the energy efficiency of each device.

[0228] In this embodiment, the characteristic parameters of the preprocessed voltage signal, current signal and carbon emission signal are synchronously mapped to a three-dimensional coordinate system to form an energy-carbon data point set, each of which corresponds to the equipment operating status and carbon emission characteristics at a specific moment.

[0229] The Euclidean distance metric calculates the spatial distance between energy-carbon data points, quantifies the comprehensive differences in voltage, current, and carbon emissions, and reflects the similarity of equipment operating conditions; the density clustering algorithm divides clusters based on the distribution density of data points, identifies dense areas as normal clusters, and characterizes the energy-carbon coupling relationship under stable operating conditions; abnormal data points that deviate from normal clusters are marked to characterize abnormal operating conditions such as excessive voltage fluctuations, abnormal harmonic distortion, or unbalanced energy-carbon coupling.

[0230] The process of establishing an energy-carbon coupling relationship model includes: calculating the correlation coefficient matrix between the three parameters of voltage, current and carbon emissions, constructing an energy-carbon coupling intensity evaluation function to quantify the degree of correlation, and determining the boundary conditions of the optimal energy efficiency operating range through historical data analysis.

[0231] When outputting analysis result data, a three-dimensional energy-carbon spatial distribution map is generated, using point cloud density and color gradient to reflect the energy-carbon coupling intensity of different regions; the degree of deviation of abnormal data points is calculated to quantify the magnitude of the difference from normal clusters; the energy-carbon coupling optimization parameter range is provided, such as the recommended upper limit of the harmonic distortion rate or the load rate adjustment range; and the equipment operation energy efficiency assessment report is output, including energy efficiency grade classification and carbon efficiency improvement suggestions.

[0232] This embodiment transforms multi-dimensional energy-carbon data into actionable decision-making basis through three-dimensional spatial mapping and quantitative modeling. It not only intuitively displays the spatiotemporal correlation between equipment operating status and carbon emissions, but also accurately locates energy efficiency bottlenecks through abnormal deviation analysis, providing multi-dimensional data support for the park to dynamically adjust energy configuration and optimize carbon emission control strategies.

[0233] In some embodiments, dynamically rendering the waveform change trend of the power parameter in the three-dimensional coordinate system includes:

[0234] Establish a time-amplitude-phase three-dimensional waveform space, which includes a time axis, an amplitude axis, and a phase axis. The time axis represents the signal acquisition time series, the amplitude axis represents the instantaneous amplitude of the voltage or current signal, and the phase axis represents the signal phase angle change;

[0235] Map the waveform data of continuously collected voltage and current signals into three-dimensional space in real time;

[0236] Use dynamic interpolation algorithm to generate smooth three-dimensional waveform surface;

[0237] Automatically adjust the 3D viewing angle and rendering frequency according to the characteristic change rate of the current signal and voltage signal;

[0238] The heat map matrix shows the relationship between energy consumption and carbon emissions of equipment in different regions, including:

[0239] Construct a two-dimensional heat map coordinate system. The two-dimensional heat map coordinate system has a horizontal axis and a vertical axis. The horizontal axis represents the regional distribution of equipment, and the vertical axis represents the time segment interval.

[0240] Calculate the energy-carbon correlation intensity coefficient of each device in the corresponding period;

[0241] The energy-carbon correlation intensity coefficient is visualized using a color gradient mapping algorithm;

[0242] Set dynamic thresholds to trigger regional energy and carbon anomaly warning signs;

[0243] Automatic storage and hierarchical display of abnormal event information based on event triggering mechanism include:

[0244] Set multi-level event trigger threshold conditions, including voltage swell / sag event trigger thresholds, harmonic excess event trigger thresholds, and energy-carbon coupling imbalance event trigger thresholds;

[0245] Establish an event hierarchical storage strategy. The event classification includes level 1 events, level 2 events, level 3 events. Level 1 events store complete waveform data in real time, level 2 events store characteristic parameter snapshots, and level 3 events only record event logs.

[0246] According to the event classification display scheme, the first-level event triggers a full-screen alarm display, the second-level event is prompted in the dedicated alarm area, and the third-level event generates a statistical report entry.

[0247] In this embodiment, a dynamic interpolation algorithm is used to smooth discrete data points to generate a continuous and interactive three-dimensional waveform rendering effect, and the viewing angle focus area and refresh frequency are automatically adjusted according to the waveform change rate to achieve dynamic tracking and display of voltage swell / sag events.

[0248] When using a heat map matrix to display the relationship between energy consumption and carbon emissions for equipment in different regions, the energy-carbon correlation intensity coefficient for each device in the corresponding time period is generated by weighting electricity consumption, carbon emissions, and energy efficiency assessment results. A color gradient mapping algorithm is used to convert the correlation intensity coefficient into thermal color blocks. High-carbon emission areas and low-carbon, high-efficiency areas are identified by color temperature gradients. Dynamic thresholds are set to trigger energy-carbon anomaly warning signs, such as flashing red to indicate areas where the correlation intensity exceeds the limit.

[0249] The event-based triggering mechanism automatically stores and displays abnormal event information in a hierarchical manner, using multi-level trigger conditions such as voltage swell / sag event thresholds, harmonic excess event thresholds, and energy-carbon coupling imbalance thresholds. Level 1 events display alarm information on a full screen and store complete waveform data. Level 2 events display parameter snapshots and a brief analysis in a dedicated alarm area on the visual interface. Level 3 events only generate statistical report entries for historical tracing. This hierarchical event storage strategy allocates storage resources based on priority, ensuring the integrity and traceability of critical event data.

[0250] This embodiment enhances the intuitive perception of waveform trends through three-dimensional dynamic rendering, the heat map matrix realizes the rapid positioning of regional energy-carbon efficiency, and the event classification mechanism optimizes the efficiency of abnormal management. The three together improve the multi-dimensional analysis capabilities of complex energy-carbon data, providing users with a complete technical chain from real-time monitoring to decision-making optimization, and supporting the precise implementation of energy-carbon coordinated regulation.

[0251] By adopting the above technical solution, the present invention is different from the existing technology and has the following beneficial effects:

[0252] The present invention realizes the real-time synchronous collection of multi-source energy and carbon data through the data acquisition module, and combines the energy and carbon coupling analysis technology of the data processing module to convert discrete voltage signals, current signals and carbon emission signals into multi-dimensional related data sets, solving the problems of isolated data and single analytical dimension in traditional systems; the visualization display module is based on three-dimensional dynamic rendering technology, and presents energy and carbon monitoring data, waveform change trends and correlation relationships in a temporal and spatial linkage visualization interface, enhancing the intuitive readability of complex data, and the interactive control module supports user-defined display perspectives and content screening to improve data interaction efficiency; the communication transmission module ensures efficient data intercommunication between the terminal and the remote management platform, and supports park-level energy and carbon collaborative management and control. The above technical solution realizes the full-link closed-loop management of energy and carbon data from collection, analysis to decision-making through multi-module collaboration, which not only improves the real-time response capability of abnormal events, but also provides accurate data support for energy efficiency optimization and carbon emission regulation, helping industrial parks to build refined and intelligent energy and carbon collaborative management systems.

[0253] In addition, the functional units in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0254] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the various embodiments of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0255] The above descriptions are only some embodiments of the present invention and do not limit the scope of protection of the present invention. Any equivalent device or equivalent process transformation made by using the contents of the description and drawings of the present invention, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A three-dimensional energy and carbon data visualization terminal, characterized in that: include: A data acquisition module is used to collect raw data in real time, including voltage signals, current signals, and carbon emission signals of the park's power equipment; A data processing module, connected to the data acquisition module, for performing energy-carbon coupling analysis on the raw data to obtain analysis results; A visual display module, connected to the data processing module, for dynamically displaying the analysis results of the energy-carbon coupling analysis; An interactive control module, connected to the visual display module, for receiving user instructions and adjusting display content; Communication transmission module, used for data interaction with the remote management platform.

2. The three-dimensional energy carbon data visualization terminal according to claim 1, characterized in that: The analysis results include energy-carbon monitoring data, waveform change trends of power parameters, the correlation between energy consumption and carbon emissions, and abnormal event information. The visual display module includes: Main display unit, used to display the main interface of real-time energy and carbon monitoring data; Waveform analysis unit, used to display the waveform change trend of power parameters; Energy-carbon mapping unit, used to establish and display the correlation between energy consumption and carbon emissions; The event recording unit is used to store and display abnormal event information.

3. The three-dimensional energy carbon data visualization terminal according to claim 2, characterized in that: The relationship between energy consumption and carbon emissions includes energy consumption data, energy utilization efficiency, and energy-carbon coupling relationship. The energy-carbon mapping unit includes: A carbon emission calculation subunit, used to calculate carbon emissions based on energy consumption data; Energy efficiency assessment subunit, used to analyze energy utilization efficiency; Multi-dimensional display subunit, used to display the energy-carbon coupling relationship in the form of a chart.

4. The three-dimensional energy carbon data visualization terminal according to claim 1, characterized in that: The data processing module includes: A data preprocessing unit, used for filtering and standardizing the original data to obtain preprocessed data; A feature extraction unit, used to extract energy-carbon feature parameters from preprocessed data; A coupling analysis unit is used to establish an energy-carbon coupling model based on energy-carbon characteristic parameters to obtain analysis results; The storage management unit is used to classify and store the processed energy-carbon characteristic parameters and analysis results.

5. A method for using a three-dimensional energy carbon data visualization terminal, characterized in that: The visualization terminal according to any one of claims 1 to 4, wherein the method comprises: Real-time collection of raw data, including voltage signals, current signals, and carbon emission signals of the park's power equipment, wherein the carbon emission signals are configured to be obtained by dynamically calculating the electricity-to-carbon conversion factor; Preprocessing the raw data, using a multi-source data spatiotemporal alignment algorithm to eliminate time delay differences, and obtaining preprocessed data; Performing feature extraction on the pre-processed data, and extracting energy-carbon feature parameters through harmonic decomposition and load feature identification; An energy-carbon coupling model is established based on energy-carbon characteristic parameters. A three-dimensional topological mapping algorithm is used to spatially correlate voltage signals, current signals, carbon emission signals, and equipment operating status to obtain analysis results. The analysis results include energy-carbon monitoring data, waveform change trends of power parameters, the correlation between energy consumption and carbon emissions, and abnormal event information. The analysis results are presented, including: Dynamically render the waveform change trend of power parameters in a three-dimensional coordinate system; The relationship between energy consumption and carbon emissions of equipment in different areas is displayed through a heat map matrix; Automatically store and hierarchically display abnormal event information based on event triggering mechanism.

6. The method for using the three-dimensional energy carbon data visualization terminal according to claim 5, characterized in that: The carbon emission signal is configured to be obtained by dynamically calculating the electricity-to-carbon conversion factor and includes: Collecting real-time operating parameters of the park's power equipment, including equipment type, load rate, and operating time; Synchronously obtain power grid emission factors and environmental monitoring data; generating a dynamic electricity-to-carbon conversion factor based on the real-time operating parameters and the grid emission factor; The real-time power consumption is calculated based on the collected voltage signal and current signal, which is expressed by formula (1). The formula (1) is as follows: In formula (1), E is the energy consumption, U(t) is the real-time voltage signal, and I(t) is the real-time current signal; The dynamic electricity-to-carbon conversion factor of the current device is matched and the real-time carbon emission signal is calculated and expressed by formula (2), which is as follows: C=E·k+C0; In formula (2), C is the real-time carbon emission signal, k is the dynamic electricity-carbon conversion factor, and C0 is the inherent carbon emission compensation value of the equipment; Monitoring the change in the load rate in the real-time operating parameters and the fluctuation in the grid emission factor; When the change value of the load rate exceeds a preset load threshold, or the fluctuation value of the grid emission factor exceeds a preset emission threshold, recalculation of the dynamic electricity-to-carbon conversion factor is triggered; Updating the dynamic electricity-to-carbon conversion factor using a time-weighted average algorithm; comparing the real-time carbon emission signal with an actual measurement value of a carbon emission monitoring device; When the comparison difference exceeds a preset error threshold, adjusting the inherent carbon emission compensation value of the equipment; The adjustment process of the dynamic electricity-to-carbon conversion factor and the inherent carbon emission compensation value of the equipment is recorded in a conversion factor database.

7. The method for using the three-dimensional energy carbon data visualization terminal according to claim 5, characterized in that: The raw data is preprocessed, and a multi-source data spatiotemporal alignment algorithm is used to eliminate time delay differences, and the preprocessed data obtained includes: Establishing a unified time base to synchronize the timestamps of the voltage signal, current signal, and carbon emission signal to a time coordinate system; A weighted average method at fixed time intervals is used to aggregate the voltage signal and the current signal so that the time resolution of the voltage signal and the time resolution of the current signal match the carbon emission signal; Delay calibration testing is used to determine the inherent transmission delay of each signal acquisition terminal, including: Measuring the first processing delay of the voltage signal acquisition terminal from signal input to data output; Measuring the second processing delay of the current signal acquisition terminal from signal input to data output; Measuring the third processing delay from signal input to data output of the carbon emission signal collection terminal; Based on the network transmission delay monitoring results, the time compensation amount of each signal is dynamically adjusted, including: Real-time monitoring of voltage signal transmission network delay fluctuations; Real-time monitoring of current signal transmission network delay fluctuations; Real-time monitoring of carbon emission signal transmission network delay fluctuations; Establish a time delay compensation matrix to perform time calibration on each signal data at each acquisition moment, ensuring that the voltage, current, and carbon emission data collected at the same moment have a strict time alignment relationship; Verify the validity of the time-aligned data, including: Verify whether the phase relationship between the voltage signal and the current signal conforms to physical laws; Verify whether the carbon emission signal matches the changing trend of electricity consumption; Eliminate data points that do not meet the verification criteria; The data that has been strictly time-aligned and validated are classified and stored according to device identification to form a pre-processed data set for subsequent analysis, wherein the pre-processed data set includes a plurality of the pre-processed data.

8. The method for using the three-dimensional energy carbon data visualization terminal according to claim 5, characterized in that: Feature extraction is performed on the pre-processed data, and energy-carbon feature parameters are extracted through harmonic decomposition and load feature identification, including: Perform fast Fourier transform on the voltage and current signals to obtain the current waveform characteristics, extract the fundamental wave and 2-65 harmonic components, calculate the amplitude, phase and harmonic distortion rate of each harmonic, and obtain the harmonic component parameters; The load characteristic recognition algorithm is used to analyze the current waveform characteristics and extract the load characteristic parameters, including: Calculate the peak-to-valley ratio and waveform factor of the current waveform; Identify the mutation points and periodic characteristics of the current waveform; Extract transient response characteristics during load switching; Establish the correlation matrix between harmonic component parameters and load characteristic parameters, and map the harmonic characteristics to the load class type; Calculate instantaneous carbon emission intensity based on carbon emission signals and conduct correlation analysis with current load characteristics; Extract energy-carbon characteristic parameters, including: Calculate carbon emissions per unit of electricity consumption; Analyze carbon emission characteristics under different load types; Establish the correlation between harmonic distortion rate and carbon emission efficiency; Obtain energy-carbon characteristic parameters; The extracted energy and carbon characteristic parameters are stored in time series.

9. The method for using the three-dimensional energy carbon data visualization terminal according to claim 5, characterized in that: An energy-carbon coupling model was established based on the energy-carbon characteristic parameters. A three-dimensional topological mapping algorithm was used to spatially correlate the voltage signal, current signal, carbon emission signal, and equipment operating status. The analysis results include: Constructing a three-dimensional energy-carbon space coordinate system, the three-dimensional energy-carbon space coordinate system including a first dimension, a second dimension, and a third dimension, wherein the first dimension corresponds to voltage characteristic parameters, including the effective value of the fundamental voltage and the harmonic voltage content rate, the second dimension corresponds to current characteristic parameters, including the effective value of the fundamental current and load characteristic parameters, and the third dimension corresponds to carbon emission characteristic parameters, including real-time carbon emissions and carbon emission intensity; Synchronously mapping the preprocessed voltage signal, current signal, and carbon emission signal characteristic parameters to a three-dimensional coordinate system to form an energy-carbon data point set, wherein the energy-carbon data point set includes a plurality of energy-carbon data points; Perform spatial cluster analysis on the energy and carbon data point set, including: The spatial distance between multiple energy-carbon data points is calculated using the Euclidean distance metric; The density clustering algorithm is used to identify the cluster of energy and carbon data points under normal working conditions, which is recorded as the normal cluster; Marking outlier data points that deviate from the normal cluster; Establish an energy-carbon coupling relationship model, including: Calculate the correlation coefficient matrix between the three parameters of voltage, current and carbon emissions; Construct an energy-carbon coupling strength evaluation function; Determine the boundary conditions of the optimal energy efficiency operating range; Output analysis result data, including: Generate a three-dimensional spatial distribution map of energy and carbon; Calculating the degree of deviation of each abnormal data point; Provides the parameter range for energy-carbon coupling optimization; Output an evaluation report on the energy efficiency of each device.

10. The method for using the three-dimensional energy carbon data visualization terminal according to claim 5, characterized in that: Dynamic rendering of the waveform change trend of power parameters in a three-dimensional coordinate system includes: Establishing a time-amplitude-phase three-dimensional waveform space, wherein the three-dimensional waveform space includes a time axis, an amplitude axis, and a phase axis, wherein the time axis represents the signal acquisition time series, the amplitude axis represents the instantaneous amplitude of the voltage or current signal, and the phase axis represents the signal phase angle change; Map the waveform data of continuously collected voltage and current signals into three-dimensional space in real time; Use dynamic interpolation algorithm to generate smooth three-dimensional waveform surface; Automatically adjust the 3D viewing angle and rendering frequency according to the characteristic change rate of the current signal and voltage signal; The heat map matrix shows the relationship between energy consumption and carbon emissions of equipment in different regions, including: Constructing a two-dimensional heat map coordinate system, wherein the two-dimensional heat map coordinate system has a horizontal axis and a vertical axis, wherein the horizontal axis represents the regional distribution position of the equipment and the vertical axis represents the time segment interval; Calculate the energy-carbon correlation intensity coefficient of each device in the corresponding period; The energy-carbon correlation intensity coefficient is visualized using a color gradient mapping algorithm; Set dynamic thresholds to trigger regional energy and carbon anomaly warning signs; Automatic storage and hierarchical display of abnormal event information based on event triggering mechanism include: Set multi-level event trigger threshold conditions, including voltage swell / sag event trigger thresholds, harmonic excess event trigger thresholds, and energy-carbon coupling imbalance event trigger thresholds; Establish an event hierarchical storage strategy, the event classification includes level one events, level two events, level one and level three events. Level one events store complete waveform data in real time, level two events store characteristic parameter snapshots, and level three events only record event logs; According to the event classification display scheme, the first-level event triggers a full-screen alarm display, the second-level event is prompted in the dedicated alarm area, and the third-level event generates a statistical report entry.

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