Power equipment full life cycle carbon footprint tracking management platform

Abstract

The invention relates to the technical field of carbon emission, in particular to a full-life-cycle carbon footprint tracking management platform for power equipment. The platform collects carbon emission parameters in the whole process from raw material input to scrap recovery in real time through a monitoring equipment network and a sensor array to generate an original monitoring data stream; performing multi-source fusion and deviation correction on the data stream to generate a standardized carbon footprint sequence; a carbon emission trend model is constructed based on sequence extraction correlation features, and a dynamic adjustment strategy is generated when an integration fracture risk or emission abnormity is detected; transmitting an abnormal signal through a mobile terminal interface, receiving an adjustment instruction, and generating a closed-loop control signal; storing the extended carbon footprint data to construct a full life cycle file; estimating emission fluctuation to generate a stability strengthening strategy; and a monitoring-analysis-control-prediction four-order optimization link is formed. According to the invention, accurate tracking and dynamic optimization of carbon emission are realized, data integrity and management timeliness are improved, and technical support is provided for green manufacturing of power equipment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of carbon emissions, and more particularly to a power equipment full life cycle carbon footprint tracking management platform. BACKGROUND

[0002] In the prior art, power equipment manufacturing is a key field of energy transformation and low-carbon development, and its carbon emission management directly affects the realization of global sustainable development goals. For the full life cycle of power equipment from raw material mining to production and manufacturing, operation and maintenance, and recycling, accurate tracking and management of carbon footprint has become an important link in the green transformation of the industry. However, the existing carbon footprint management methods have significant deficiencies in dynamic and systematicity. Most solutions rely on post-statistics or sampling detection, which is difficult to capture dynamic emission data in the production process in real time, and lacks integrated analysis of multi-link emissions in complex manufacturing processes, resulting in incomplete carbon footprint data and difficulty in providing reliable basis for decision-making.

[0003] In the power equipment manufacturing supervision link, the core challenge is to realize real-time carbon emission monitoring and data integration of the whole production process. The manufacturing process involves multiple processes, multiple raw materials and energy-consuming equipment, and the emission sources are complex and dynamically changing, so the existing monitoring means is difficult to accurately quantify the carbon emissions of each process. For example, in the equipment welding process, energy consumption fluctuations and auxiliary material use may cause instantaneous changes in emission data, but traditional monitoring equipment has low update frequency and is difficult to record these changes in real time, resulting in data deviation. In addition, the supervision personnel need to frequently switch between the production site and the management terminal, and lack a convenient mobile terminal data acquisition method, resulting in lag in recording emission parameters of key processes, making it difficult to form a complete carbon footprint file from raw materials to finished products. This real-time monitoring deficiency and data integration breakage problem together hinder the accurate management of carbon footprint.

[0004] Therefore, how to deploy efficient real-time carbon emission monitoring equipment in the power equipment manufacturing supervision link and combine with mobile terminal technology to realize dynamic data acquisition and complete file construction from raw material input to finished product delivery has become a key problem in promoting the construction of full life cycle carbon footprint management platform. SUMMARY

[0005] In view of this, the present application provides a power equipment full life cycle carbon footprint tracking management platform, aiming to solve the problems of the existing carbon footprint management method in dynamic and systematic deficiencies, specifically including most solutions rely on post statistics or sampling detection, it is difficult to capture dynamic emission data in the production process in real time, lack of integrated analysis of multi-link emissions in complex manufacturing process leads to incomplete carbon footprint data, and in the manufacturing supervision link of power equipment manufacturing, the existing monitoring means is difficult to accurately quantify the carbon emissions of each process, the lack of convenient mobile data acquisition method of supervision personnel leads to the lag of emission parameter recording of key processes and the formation of complete carbon footprint archives from raw materials to finished products, etc., so as to improve the accuracy and dynamic optimization ability of carbon emission management in the manufacturing supervision link, and provide efficient technical support for green manufacturing.

[0006] In order to achieve the above purpose, the present application adopts the following technical solutions:

[0007] A power equipment full life cycle carbon footprint tracking management platform, characterized in that it comprises:

[0008] A full life cycle data acquisition unit configured to acquire carbon emission parameters in the whole process from raw material input, manufacturing processing, finished product delivery to scrap recycling in real time through a monitoring device network and a sensor array deployed in the manufacturing supervision link, and generate original monitoring data stream;

[0009] A data processing and integration unit connected to the acquisition unit and configured to perform multi-source data fusion and deviation correction on the original monitoring data stream, and generate standardized carbon footprint sequence;

[0010] A carbon footprint analysis and management unit configured to extract full life cycle correlation features based on the standardized sequence, build a carbon emission trend model, and generate dynamic adjustment strategies when detecting integrated fracture risk or emission anomalies;

[0011] A closed-loop control unit configured to realize two-way interaction through a mobile terminal interface, including: transmitting carbon emission anomaly signals, receiving dynamic acquisition adjustment instructions, and fusing feedback data to generate closed-loop control signals;

[0012] An archive management unit configured to store and extend carbon footprint data, and build a carbon footprint archive covering the whole life cycle;

[0013] A prediction and optimization unit configured to estimate emission fluctuations based on the extended carbon footprint archive, identify manufacturing link optimization points, and generate stability strengthening strategies;

[0014] The data processing and integration unit, the carbon footprint analysis and management unit, the closed-loop control unit and the prediction and optimization unit are sequentially connected to form a four-order dynamic optimization link of monitoring-analysis-control-prediction.

[0015] In one specific implementation, the full life cycle data acquisition unit comprises:

[0016] A raw material input monitoring subunit configured to acquire initial carbon emission parameters;

[0017] A multi-process sensor subunit configured to monitor real-time emission data of manufacturing processes;

[0018] A mobile terminal feedback subunit configured to trigger the reporting of carbon emission data of the scrap recycling stage through geofencing technology.

[0019] In one specific implementation, the data processing and integration unit performs:

[0020] Integrates multi-process carbon emission data to generate a preliminary sequence using Kalman filtering algorithm;

[0021] When the sequence deviation exceeds the preset threshold, a weighted average correction mechanism is triggered, and the weight coefficient is dynamically allocated based on the historical record of device accuracy;

[0022] Outputs a standardized carbon footprint sequence with deviation eliminated.

[0023] In one specific implementation, the carbon footprint analysis and management unit comprises:

[0024] A feature extraction module configured to extract key correlation features from the carbon footprint sequence using principal component analysis;

[0025] A trend modeling module configured to construct a carbon emission trend model using time series analysis algorithm;

[0026] An abnormal decision module configured to generate adjustment strategies when the model fracture risk index exceeds the threshold or the real-time emission value continuously exceeds the standard.

[0027] In one specific implementation, the closed-loop control unit performs:

[0028] Pushes a hierarchical early warning signal to the mobile terminal;

[0029] Analyzes the adjustment instructions fed back by the terminal and issues them to the sensor array of the specified process;

[0030] Fuses the scrap recycling feedback data to generate a closed-loop control signal covering the entire life cycle.

[0031] In one specific implementation, the archive management unit comprises:

[0032] A basic archive library storing the standardized carbon footprint sequence of the manufacturing stage;

[0033] An extended carbon footprint record is generated by the extended record generation module, which dynamically aggregates the carbon footprint records of products from manufacturing to recycling in response to the closed-loop control signal.

[0034] In one specific implementation, the trend modeling module employs an ARIMA algorithm:

[0035] The carbon footprint sequence is differenced and stationary;

[0036] An autoregressive and moving average model is fitted by an adaptive parameter estimation algorithm;

[0037] The carbon emission prediction value and the fracture risk probability index are output.

[0038] In one specific implementation, when the anomaly decision module activates the data verification mechanism:

[0039] The carbon emission patterns of similar devices in the historical database are associated;

[0040] The continuous trend value is fitted using a linear regression algorithm;

[0041] When the continuity index of the repaired data set exceeds the preset threshold, the model accuracy is maintained.

[0042] In one specific implementation, the prediction optimization unit performs:

[0043] Based on the extended carbon footprint record, a random forest algorithm is used to fuse meteorological and working condition factors to predict emission fluctuations;

[0044] Potential optimization points in the manufacturing process are identified;

[0045] The stability enhancement strategy, which includes the collection frequency adjustment parameter and the sensor deployment optimization scheme, is output to the closed-loop control unit.

[0046] In one specific implementation, the random forest algorithm performs:

[0047] The historical emission data is fitted by multiple decision trees in multiple dimensions;

[0048] The future emission fluctuation curve and the dynamic risk threshold are generated;

[0049] When the fluctuation exceeds the threshold, the key optimization nodes in the manufacturing supervision process are located based on the decision tree splitting features.

[0050] Compared with the prior art, the power equipment full life cycle carbon footprint tracking management platform provided by the application is used for tracking and managing the carbon footprint of power equipment in the full life cycle, realizes accurate tracking, dynamic management and optimization of the full life cycle carbon footprint from raw material input to scrap recycling, effectively improves the accuracy and dynamic optimization capability of carbon emission management in the manufacturing supervision link, strengthens the stability of dynamic data acquisition, provides efficient technical support for green manufacturing, and has the following beneficial effects:

[0051] The application can effectively capture carbon emission data of each link in the full life cycle of power equipment, and improves the accuracy of monitoring.

[0052] The application fuses and corrects multi-source data, solves the problem of carbon footprint data fragmentation, and ensures the accuracy and consistency of data.

[0053] The application realizes closed-loop management of "monitoring-analysis-control-prediction", accelerates the response speed of instructions through mobile terminal interaction, shortens the traditional 4 hours to 10 minutes, and improves the timeliness of management. BRIEF DESCRIPTION OF DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the provided drawings.

[0055] Figure 1 The system architecture diagram of the power equipment full life cycle carbon footprint tracking management platform is provided.

[0056] Figure 2 The multi-source data fusion and deviation correction flowchart is provided.

[0057] Figure 3 The mobile terminal interaction and closed-loop control timing diagram is provided.

[0058] Figure 4 The emission fluctuation prediction and optimization positioning flowchart is provided.

[0059] Figure 5 The full life cycle file extension logic diagram is provided. DETAILED DESCRIPTION

[0060] The technical solutions in the embodiments of the present application will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work are within the scope of protection of the present application.

[0061] The present application relates to a power equipment full life cycle carbon footprint tracking management platform. The hardware configuration, workflow and implementation logic of the core technology of the platform are described one by one below.

[0062] I. Overall operation logic of the platform

[0063] The power equipment full life cycle carbon footprint tracking management platform of the present application realizes full-process management through a four-stage dynamic optimization link of "monitoring-analysis-control-prediction", as shown in Figure 1 Each core unit is sequentially connected to form a closed-loop architecture.

[0064] Monitoring layer: The full life cycle data acquisition unit acquires real-time full-process carbon emission parameters from raw material input to scrap recycling through a sensor array and a monitoring device network.

[0065] Analysis layer: The data processing and integration unit fuses and corrects the original data to generate a standardized carbon footprint sequence, and then the carbon footprint analysis and management unit extracts features and builds a trend model.

[0066] Control layer: The closed-loop control unit realizes abnormal early warning and instruction adjustment through mobile terminal interaction to generate a closed-loop control signal.

[0067] Prediction layer: The prediction optimization unit estimates emission fluctuations based on an extended carbon footprint archive to generate a stability enhancement strategy, which feeds back to the monitoring and control links.

[0068] Each unit is sequentially connected to form a dynamic optimization closed loop, ensuring the accuracy, integrity and timeliness of carbon emission data management.

[0069] II. Specific implementation of core units

[0070] (1) Full life cycle data acquisition unit

[0071] This unit realizes full life cycle data acquisition through three sub-systems, and the hardware and process are as follows:

[0072] Hardware configuration: A high-precision carbon emission sensor (measurement range 0-5000ppm, accuracy ±2%) with model number SEN-CO2-900 is used to build a sensor array, which is combined with a NET-485 industrial bus gateway to form a monitoring device network, and a mobile terminal is adapted with APP-CO2 management software (compatible with Android / iOS system).

[0073] Collection process:

[0074] Raw material input monitoring: Sensors are deployed in the storage areas for raw materials such as steel and copper to collect initial carbon emission parameters from transportation (such as truck fuel emissions) and storage, with a sampling frequency of 1 time per minute.

[0075] Manufacturing and processing monitoring: Sensors are deployed in welding, assembly, painting and other processes. The welding process uses the high-temperature-adapted SEN-CO2-900-H, with a sampling frequency of 10 times / second, to capture instantaneous emission fluctuations during arc welding in real time;

[0076] Scrap recycling monitoring: Recycling site geo-fences are demarcated using GPS (accuracy ±5 meters). When a recycling vehicle enters the fence, the APP-CO2 software automatically triggers the portable sensor SEN-CO2-900-P to report disassembly and transportation data.

[0077] Geographic fencing technology triggers data reporting through positioning, avoiding data omissions during the scrapping stage; the high sampling frequency ensures that instantaneous emissions from processes such as welding (such as CO2 peaks caused by current fluctuations) are not ignored, solving the problem of lagging updates for traditional monitoring equipment.

[0078] (2) Data processing and integration unit

[0079] This unit fuses and corrects multi-source data. The specific implementation is as follows:

[0080] Hardware configuration: SRV-2023 industrial server (Intel Xeon E5 processor, 32GB memory) with built-in KF-2023 Kalman filter module.

[0081] Processing flow:

[0082] Multi-source fusion: Figure 2 As shown in the figure, the Kalman filter algorithm is used to integrate heterogeneous data (such as sensor data and manually entered data) from raw materials, manufacturing, and recycling to generate a preliminary carbon footprint sequence;

[0083] Deviation correction: When the preliminary sequence deviates from the historical baseline by more than 5% (preset threshold), a weighted average correction is triggered – weights are assigned based on the device accuracy history (e.g., 0.8 for the SEN-CO2-900 sensor and 0.2 for the auxiliary sensor);

[0084] Output standardized sequence: The corrected data is stored in JSON format, including fields such as timestamp (accurate to milliseconds), process ID, and emission value.

[0085] Kalman filter reduces data noise (such as electromagnetic interference during welding) through "prediction-update", and the weighted correction mechanism dynamically allocates weights based on device accuracy, ensuring the accuracy and consistency of the sequence.

[0086] Specifically, Kalman filter first predicts the state at the current time according to the state at the last time to obtain a priori estimate, and then corrects the a priori estimate by combining the measurement value at the current time to obtain a posteriori estimate. Through continuous iteration of these two steps, the data is filtered to reduce data noise, such as the influence of electromagnetic interference during welding on measurement data. Kalman filter algorithm formula and derivation:

[0087] Let the state equation of the system be:

[0088] x k =Ax k-1 +Bu k-1 +w k-1

[0089] The measurement equation is:

[0090] z k =Hx k +v k

[0091] Where x k is the system state at time k, A is the state transition matrix, B is the control input matrix, u k-1 is the control input at time k-1, w k-1 is the process noise, z k is the measurement value at time k, H is the measurement matrix, v k is the measurement noise.

[0092] Prediction step:

[0093] A priori state estimate:

[0094]

[0095] A priori estimate covariance:

[0096] P k|k-1 =AP k-1|k-1 A T +Q

[0097] Where Q is the process noise covariance.

[0098] Update step:

[0099] Kalman gain:

[0100] K k =P k|k-1 H T (HPk|k-1 H T +R) -1

[0101] where R is the measurement noise covariance.

[0102] Posterior state estimate:

[0103]

[0104] Posterior estimate covariance:

[0105] P k|k = (I - K k H)P k|k-1

[0106] According to the accuracy history of each device, the higher the accuracy of the device, the higher the weight is given to reduce the influence of measurement error of low accuracy devices on the result. By weighted average, the measurement values of multiple devices are integrated to get more accurate correction results. Specifically as follows:

[0107] Deviation correction: when the preliminary sequence deviates from the historical reference value by more than 5% (preset threshold), trigger weighted average correction - assign weights based on device accuracy history (such as SEN-CO2-900 sensor weight 0.8, auxiliary sensor 0.2).

[0108] Weighted average correction formula:

[0109]

[0110] where x corr is the corrected result, w i is the weight of the i-th device, x i is the measurement value of the i-th device, and

[0111] (3) Carbon footprint analysis management unit

[0112] This unit is responsible for feature extraction, trend modeling and abnormal decision, the specific implementation is as follows:

[0113] Hardware configuration: ANL-500 edge computing device (NVIDIA Jetson AGX processor) is adopted, which integrates PCA feature extraction module and ARIMA algorithm library.

[0114] Analysis process:

[0115] Feature extraction: principal component analysis (PCA) is used to extract key features (such as "welding energy consumption peak" "coating emission average") from the standardized sequence, and 80% information is retained to simplify the model;

[0116] Set m samples, each sample has n characteristics, constitute the data matrix X m×n .

[0117] First, the data is standardized:

[0118]

[0119] Where is the mean of the jth feature, s j is the standard deviation of the jth feature.

[0120] Calculate the covariance matrix:

[0121]

[0122] Eigenvalue decomposition of the covariance matrix:

[0123] Cv = λv

[0124] Where λ is the eigenvalue, v is the corresponding eigenvector.

[0125] Select the eigenvector corresponding to the first k largest eigenvalues to form the projection matrix V n×k .

[0126] Get the principal component:

[0127] Y = X'V

[0128] Trend modeling: based on ARIMA algorithm (parameters p = 2, d = 1, q = 2) to build the model - 1 order difference stationary processing to the sequence, through the adaptive parameter estimation algorithm fitting autoregressive and moving average term, output future 24 hours emission prediction value and fracture risk probability (such as equipment downtime caused by data interruption probability);

[0129] ARIMA(p,d,q) model formula:

[0130] First, the non-stationary sequence is d-order difference, and the stationary sequence y' is obtained t .

[0131] ARIMA model can be expressed as:

[0132]

[0133] Where c is the constant term, φ i is the autoregressive coefficient, θ j is the moving average coefficient, ε t is a white noise sequence.

[0134] The process is for non-stationary time series, by d order difference to make it stationary, and then use AR(p) model to describe the autoregressive relationship of the sequence, and use MA(q) model to describe the moving average relationship of the sequence, so as to construct ARIMA model to predict the trend of the sequence.

[0135] Abnormal decision: when the model fracture risk index > 0.7 (threshold) or the real-time emission exceeds the standard for 3 times in a row, activate the data verification mechanism-correlate the emission pattern of similar devices in the historical database, use linear regression algorithm to fit the continuous trend value, if the continuity index of the repaired data set > 90% (threshold), maintain the model accuracy and generate adjustment strategy (such as increasing the sampling frequency).

[0136] Linear regression algorithm formula:

[0137]

[0138] Where is the predicted value, x is the independent variable, a is the intercept, and b is the slope.

[0139] Solve a and b by least squares method:

[0140]

[0141] Where n is the sample size, is the mean of x, is the mean of y.

[0142] Linear regression describes the relationship between independent variables and dependent variables by finding the best fitting straight line, so that the sum of the squared errors between actual values and predicted values is minimized. The parameters of the straight line are solved by least squares method to realize the fitting of continuous trend value.

[0143] ARIMA algorithm is suitable for trend prediction of time series data, which can identify the emission rules of daily production peak (such as 8:00-10:00); data verification mechanism completes the broken sequence through historical data to ensure the stability of the model.

[0144] (4) Closed loop control unit

[0145] This unit realizes two-way interaction and closed loop control, the specific implementation is as follows:

[0146] Hardware configuration: use CTL-800 industrial PLC (support Modbus TCP / IP protocol) as the core controller, and mobile terminal interface adapts APP-CO2 software.

[0147] Control process:

[0148] Early warning push: such as Figure 3As shown, the platform realizes two-way interaction through the mobile terminal interface, firstly pushing graded warning signals to the mobile terminal (level 1: exceeding the threshold by 20%, red; level 2: exceeding the threshold by 10%, orange; level 3: exceeding the threshold by 5%, yellow);

[0149] Command interaction: Analyze the adjustment instructions fed back by the terminal (such as "adjust the welding current to 250A") and send them to the sensor array of the corresponding process;

[0150] Closed-loop signal generation: Integrate feedback data from the scrap recycling stage to generate closed-loop control signals covering the entire life cycle from raw materials to recycling.

[0151] The graded warning system enables managers to quickly judge the severity of abnormalities. Mobile terminal interaction solves the cumbersome "site-terminal" switching problem in traditional supervision and improves the command response speed (from the traditional 4 hours to within 10 minutes).

[0152] (5) Archives Management Unit

[0153] This unit builds and expands the full life cycle archive, specifically implemented as follows:

[0154] Hardware configuration: Adopt STO-10T distributed storage server (10TB SSD, RAID5 redundancy) to ensure long-term and stable data storage.

[0155] Archive construction process:

[0156] Basic files: such as Figure 5 As shown, the archive management unit first stores the standardized carbon footprint sequence of the manufacturing stage, classified by production batches and processes (e.g., “2023-10 Batch - Welding Process”);

[0157] File extension: In response to closed-loop control signals, the carbon footprint records of finished products from leaving the factory to scrapping and recycling are aggregated according to the time and space dimensions (time: production / recycling date; space: factory area / recycling point) to form a complete "raw materials-manufacturing-recycling" file.

[0158] Aggregation of time and space dimensions ensures the continuity of archives and solves the problems of carbon footprint data fragmentation and difficulty in tracing the entire life cycle in existing technologies.

[0159] (6) Prediction and optimization unit

[0160] This unit estimates emission fluctuations and generates an optimization strategy, which is implemented as follows:

[0161] Hardware configuration: Adopts AI-Box-300 artificial intelligence computing box (8-core ARM processor), supporting parallel computing of random forest algorithm.

[0162] Prediction and optimization process:

[0163] Emission fluctuation prediction: Based on the extended carbon footprint archive, a random forest algorithm (50 decision trees) is used to integrate meteorological data (temperature, humidity) and working condition data (equipment load) to generate a 7-day emission fluctuation curve and dynamic risk threshold. Figure 4

[0164] Optimization point identification: When the fluctuation exceeds the threshold, the key optimization node is located based on the decision tree splitting features (such as "emission increases sharply when welding current > 300A").

[0165] Strategy generation: Output stability enhancement strategies including acquisition frequency adjustment parameters (such as increasing welding process from 10 times / sec to 20 times / sec) and sensor deployment schemes (such as adding 2 sensors in the painting process), and issue to the closed-loop control unit.

[0166] The random forest algorithm reduces prediction bias through multi-tree fitting, integrates meteorological and working condition factors to improve prediction accuracy, and identifies optimization points (such as high energy consumption processes) in advance to enhance dynamic management.

[0167] Random forest is composed of multiple decision trees. For classification problems, the final result is determined by voting of multiple trees; for regression problems, the final result is the average of multiple tree prediction values. Bootstrap sampling method is used to select training samples for each decision tree, and at each node split, a portion of features are randomly selected for optimal feature selection. By integrating multiple decision trees, the risk of overfitting of a single tree is reduced, and the accuracy of prediction is improved. Meteorological data and working condition data are integrated as input features, and through parallel computing of multiple decision trees, the emission fluctuation curve and dynamic risk threshold for the next 7 days are obtained.

[0168] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other. The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.​

Claims

1. A carbon footprint tracking and management platform for the entire life cycle of power equipment, characterized by: include: The full life cycle data collection unit is configured to collect carbon emission parameters from raw material input, manufacturing and processing, finished product delivery to scrapping and recycling in real time through a monitoring equipment network and sensor array deployed in the manufacturing and supervision links, generating a raw monitoring data stream; a data processing and integration unit, connected to the acquisition unit, configured to perform multi-source data fusion and bias correction on the original monitoring data stream to generate a standardized carbon footprint sequence; A carbon footprint analysis management unit, configured to extract full life cycle correlation features based on standardized sequences, build a carbon emission trend model, and generate dynamic adjustment strategies when integration disruption risks or emission anomalies are detected; A closed-loop control unit is configured to achieve two-way interaction through a mobile terminal interface, including: transmitting carbon emission abnormality signals, receiving dynamic acquisition adjustment instructions, and fusing feedback data to generate closed-loop control signals; An archive management unit configured to store and expand carbon footprint data and build a carbon footprint archive covering the entire life cycle; A prediction optimization unit configured to estimate emission fluctuations based on the extended carbon footprint profile, identify optimization points in the manufacturing process, and generate stability enhancement strategies; Among them, the data processing and integration unit, the carbon footprint analysis and management unit, the closed-loop control unit and the prediction and optimization unit are connected in sequence to form a four-order dynamic optimization link of monitoring-analysis-control-prediction.

2. The electric power equipment full life cycle carbon footprint tracking and management platform according to claim 1 is characterized in that: The full life cycle data acquisition unit includes: The raw material input monitoring subunit is configured to collect initial carbon emission parameters; Multi-process sensor subunits configured to monitor emissions data from manufacturing processes in real time; The mobile terminal feedback subunit is configured to trigger the reporting of carbon emission data in the scrapping and recycling stage through geo-fencing technology.

3. The electric power equipment full life cycle carbon footprint tracking and management platform according to claim 1 is characterized in that: The data processing and integration unit performs: The Kalman filter algorithm is used to integrate the carbon emission data of multiple processes to generate a preliminary sequence; When the sequence deviation exceeds the preset threshold, the weighted average correction mechanism is triggered, and the weight coefficient is dynamically assigned based on the device accuracy history record; Outputs a normalized carbon footprint sequence that eliminates bias.

4. The electric power equipment full life cycle carbon footprint tracking and management platform according to claim 1 is characterized in that: The carbon footprint analysis management unit includes: a feature extraction module configured to extract key correlation features from the carbon footprint sequence using principal component analysis; a trend modeling module configured to construct a carbon emission trend model using a time series analysis algorithm; The abnormal decision module is configured to generate an adjustment strategy when the model fracture risk index exceeds a threshold or the real-time emission value exceeds the standard continuously.

5. The electric power equipment full life cycle carbon footprint tracking and management platform according to claim 1 is characterized in that: The closed-loop control unit performs: Push graded warning signals to mobile terminals; Analyze the adjustment instructions fed back by the terminal and send them to the sensor array of the designated process; Integrate scrap recycling feedback data to generate closed-loop control signals covering the entire life cycle.

6. The electric power equipment full life cycle carbon footprint tracking and management platform according to claim 1 is characterized in that: The file management unit includes: A basic archive that stores standardized carbon footprint sequences during the manufacturing phase; The extended archive generation module responds to closed-loop control signals and dynamically aggregates the carbon footprint records of finished products from leaving the factory to scrapping and recycling in time and space dimensions.

7. The electric power equipment full life cycle carbon footprint tracking and management platform according to claim 4 is characterized in that: The trend modeling module uses the ARIMA algorithm: Perform differential stabilization on the carbon footprint series; Fitting autoregressive and moving average models through adaptive parameter estimation algorithms; Output carbon emission prediction value and fracture risk probability index.

8. The electric power equipment full life cycle carbon footprint tracking and management platform according to claim 4 is characterized in that: When the abnormal decision module activates the data verification mechanism: Correlate carbon emission patterns of similar equipment in historical databases; Linear regression algorithm was used to fit continuous trend values; The model accuracy is maintained when the continuity index of the repaired dataset exceeds the preset threshold.

9. The electric power equipment full life cycle carbon footprint tracking and management platform according to claim 1 is characterized in that: The prediction optimization unit performs: Based on the extended carbon footprint archive, the random forest algorithm is used to integrate meteorological and operating conditions factors to estimate emission fluctuations; Identify potential optimization points in the manufacturing process; The output includes the stability enhancement strategy of the acquisition frequency adjustment parameters and the sensor deployment optimization plan to the closed-loop control unit.

10. The electric power equipment full life cycle carbon footprint tracking and management platform according to claim 1, characterized in that: The Random Forest algorithm performs: Perform multi-dimensional split fitting on historical emission data through multiple decision trees; Generate future emission fluctuation curves and dynamic risk thresholds; When the fluctuation exceeds the threshold, the key optimization nodes in the manufacturing supervision link are located based on the decision tree splitting features.

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