383 results about "Discharge data" patented technology

The invention relates to the technical field of smart ports and low-carbon environmental protection, and discloses a green low-carbon port monitoring and evaluation system, which comprises an Internet of Things data acquisition module for acquiring multi-dimensional environmental data, carbon emission data and energy consumption data in real time through multi-type sensor equipment deployed at a port; the data processing and analysis module is used for cleaning and preprocessing the collected data, and mining the relationship between energy use and carbon emission by using a big data analysis technology and an artificial intelligence algorithm; the carbon footprint calculation module is used for calculating the carbon emission of the port activity through an accurate model according to the processed data; by deploying multiple types of sensors, multi-dimensional data in port operation can be acquired in real time, a comprehensive carbon emission evaluation system is formed, the limitation that a traditional method can only monitor a single parameter is overcome through the multi-dimensional data comprehensive acquisition mode, and the problem of information fragmentation is effectively solved; and a solid foundation is provided for subsequent data analysis and decision making.

The invention discloses a block chain-based carbon emission factor data traceability management system, particularly relates to the technical field of cross-supply-chain carbon data management, and is used for solving the problem that cross-enterprise / cross-industry effective association of carbon emission data under an existing single-chain architecture cannot be realized. The problems that the whole life cycle carbon emission data of the same product is repeatedly calculated and accounting boundaries are overlapped are solved; the method comprises the following steps: acquiring a carbon emission factor data packet of each participant through a data acquisition module; the block evidence storage module writes the data packet into a block chain to generate a non-tampering block; the cross-chain identification module creates a cross-chain traceability identifier containing a product batch number and an arrangement stage mark for the block; the coherence detection module analyzes data coherence during cross-chain request, and if the data coherence is lower than a threshold value, cross-chain verification based on matching of the mark and the batch number is triggered; the batch verification module executes repeatability verification on data in different stages of the same batch after verification is passed; and the traceability integration module integrates the verification data to generate a global carbon footprint traceability record and stores the global carbon footprint traceability record to a cross-chain shared account book.

The invention relates to the technical field of sewage plant-oriented carbon neutralization regulation and control scheme design, in particular to a sewage plant-oriented carbon neutralization regulation and control method and a sewage plant-oriented carbon neutralization regulation and control system. The method comprises the steps of collecting water quality, energy consumption and gas emission data in real time through a multi-source sensor, inputting the data into a space-time diagram convolutional network to predict a carbon emission trend after edge computing node cleaning, and dynamically optimizing an aeration rate and a sludge reflux ratio based on a fuzzy PID controller. A whole-process carbon footprint is tracked by adopting a life cycle evaluation method, a multi-target compensation strategy is generated in combination with an improved NSGA-III algorithm, and photovoltaic, biogas and green power resources are scheduled preferentially. A digital twin platform and a block chain evidence storage module are innovatively integrated, the former calibrates model parameters in real time, and the latter generates a non-tampering carbon neutralization voucher. According to the method, the problems of one-sided data, regulation and control lag and insufficient credibility of a traditional method are solved, and minute-level sensing, prediction and offset closed-loop control of carbon emission in the sewage treatment process is realized.

The invention discloses an edge computing gateway device for multi-modal energy carbon emission comprehensive monitoring, and relates to the related technical field of carbon emission monitoring, and the device comprises a carbon emission data obtaining module which is used for obtaining a plurality of carbon emission sources and a plurality of carbon emission monitoring index sets of an industrial park; the priority evaluation module is used for carrying out priority evaluation to obtain a plurality of monitoring index sequences; the carbon emission source analysis module is used for analyzing and acquiring a plurality of predicted emission trends and a plurality of emission fluctuation degrees; and the carbon emission monitoring module is used for configuring an optimized data acquisition strategy of the edge computing gateway and executing carbon emission monitoring in a preset time zone. The technical problem that the carbon emission monitoring response speed is poor due to the fact that the monitoring range is limited, the index priority is unknown, data processing lags behind and an acquisition strategy is rigid in the prior art is solved, and the technical effects of comprehensive and accurate monitoring, resource optimization configuration, real-time trend prediction, strategy dynamic adjustment and carbon emission monitoring response speed improvement are achieved.

The invention discloses an energy storage battery health feature extraction and state evaluation method based on transfer learning. The method comprises the following steps: S1, constructing a source domain health feature library and pre-training a model; according to the method, dependence on complete cyclic data is broken through, high precision and robustness are still achieved under the conditions of data sparsity and working condition difference, and the method is suitable for intelligent operation and maintenance and predictive maintenance of an energy storage power station. And meanwhile, common incomplete and partial charge and discharge data fragments under actual working conditions can be directly utilized for feature extraction and state evaluation, dependence on complete charge and discharge cycles is avoided, and the application scene of the data driving method is greatly widened.

The invention relates to the technical field of environment management, in particular to a carbon emission data management method and device based on a block chain, and the method comprises the following steps: screening missing information data based on the block chain, eliminating abnormal calculation efficiency, extracting user behavior analysis stability, adjusting authority mapping to generate a credible index, screening illegal nodes, and writing the illegal nodes into a tracking block. And comparing policy marking abnormity to obtain a compliance state. According to the method and the system, the data with missing identity labels and timestamps are screened out, the data validity ratio is improved, the timeliness and the credibility of written information are ensured, the uploading action is identified, the frequency and the operation interval are modified, the node behavior stability is described, the compliance degree is reflected, the access permission is dynamically adjusted according to the stability, and the hyper-permission operation records are screened out; high-frequency violation writing behaviors are detected and recorded, the real-time monitoring capability is enhanced, abnormity is dynamically marked, compliance evaluation and response of carbon emission data are achieved, and the credibility, precision and intelligent level of data management are comprehensively improved.

The invention relates to a community demand response carbon emission reduction data monitoring method based on the Internet of Things technology, and the method comprises the following steps: S1, collecting multi-source heterogeneous data of community carbon emission data, and carrying out the preprocessing, and obtaining a basic data set; s2, performing feature engineering and depth representation according to the basic data set, and constructing a high-dimensional feature vector set; s3, constructing a new energy output prediction model, and predicting a new energy output confidence interval based on the high-dimensional feature vector set; s4, constructing a load demand elastic prediction model, and predicting a rigid load and flexible load adjustment boundary based on the high-dimensional feature vector set; s5, based on the predicted new energy output confidence interval and the rigid load and flexible load adjustment boundary, adopting multi-objective optimization control to obtain an optimal equipment control instruction set; and S6, performing control based on the optimal equipment control instruction set, obtaining the verification emission reduction amount and a user response log, and generating a carbon perbenefit integral. According to the invention, the community can flexibly adjust the energy supply strategy according to the new energy volatility and the user demand.

The invention discloses a carbon metering method and device based on measured data and a medium, and the method comprises the steps: obtaining emission data through a preset sensor network, determining the energy consumption data of an enterprise, and obtaining environment data through a preset integrated sensor; fusing the emission data, the energy consumption data and the environment data to integrate data from different sources into a preset time coordinate system, and performing spatial aggregation on the fused data according to a production unit to determine a comparison model; determining a direct emission amount according to the emission data, determining an indirect emission amount according to the energy consumption data, comparing the direct emission amount with the indirect emission amount through a comparison model to determine an emission deviation, and comparing the emission deviation with a preset deviation threshold value; and if the emission deviation is greater than a deviation threshold value, determining a deviation reason according to the equipment operation log and the environment data, determining a re-check data point according to the deviation reason, and performing re-check metering according to the re-check data point.

The invention discloses an air pollution intelligent early warning method and system based on machine learning, and the method comprises the steps: obtaining the multi-source data of environment, wind direction, geography, traffic, emission, population and the like, carrying out the multi-scale decomposition of the environment data, extracting the short-term fluctuation and long-term trend, and achieving the time series prediction in combination with a historical mode; constructing a regional association graph by using a graph convolutional network, identifying cross-regional diffusion features, and fusing wind direction and space information to determine a high-risk region and a potential diffusion path; carrying out weighted calculation in combination with traffic and emission data to obtain a comprehensive risk score, and completing region division and early warning level determination; and finally, generating a regional visual early warning report. According to the invention, pollution trend accurate prediction and diffusion path identification can be realized.

The invention provides an accurate traceability method, device and equipment based on high-resolution air quality monitoring data and a storage medium, and relates to the technical field of atmospheric pollution source tracking. The method comprises the steps of simulating a meteorological phenomenon from a weather scale to a regional scale in a real meteorological environment based on a meteorological field simulation model, and determining high-resolution meteorological field data; inputting the high-resolution meteorological field data into a Lagrange particle tracking random transmission model so as to drive virtual particles to move in a meteorological field corresponding to the high-resolution meteorological field data, and obtaining movement tracks of the virtual particles; and determining pollution source information in the real meteorological environment based on pollutant monitoring data of the monitoring point, preset emission data and the motion trail of the virtual particles. According to the embodiment of the invention, the defect of low accuracy of pollutant traceability in the prior art is overcome, and a more accurate pollution source positioning and traceability result can be provided through combination of high-resolution meteorological simulation and a random transmission model.

The invention discloses a multi-user carbon emission data uplink method, device and system, and a storage medium. The method comprises the following steps: S1, obtaining user power consumption data of a park user node; s2, obtaining carbon emission data according to the power utilization data of the user; s3, through a block chain consensus mechanism, storing the carbon emission data in a block head structure of the block chain in a Merkel tree form; and S4, performing asymmetric encryption and digital signature on the carbon emission data. According to the technical scheme of the invention, a park carbon data link up-link and down-link collaborative storage mechanism is adopted, and whether the data in the block body is tampered or not can be confirmed by verifying the block head Merkel root; and through a consensus mechanism, multi-user computing node differences in the park are eliminated, and unification of uplink data storage rules is realized.

The invention relates to the technical field of cable carbon footprints, and discloses a cable full life cycle carbon footprint determination method. The method comprises the following steps: acquiring carbon emission data of all stages of raw material acquisition, production and manufacturing, transportation and distribution, installation and use and scrap recovery in the whole life cycle of the cable through a preset data acquisition system; establishing a multi-dimensional characterization model of the carbon footprint features by using the acquired data; performing life cycle stage adaptability evaluation on the model to generate a carbon footprint contribution degree distribution map; the semantic content of the cable product specification parameters is analyzed, the environmental influence weight corresponding to semantics is deduced, and carbon footprint calculation constraint conditions are formed in combination with the carbon footprint contribution degree distribution map; and generating a carbon footprint determination scheme by using an optimization algorithm according to the constraint condition, and outputting the carbon footprint according to the scheme. The method can systematically integrate cable full life cycle carbon emission related information, and provides a clear path for carbon footprint determination.

The invention discloses an electric two-wheeled vehicle charging method and device with an intelligent fault diagnosis function and a storage medium, and relates to the technical field of electric vehicle charging fault diagnosis, the method has an intelligent fault diagnosis function, various data in the charging process can be monitored and analyzed in real time, potential faults can be recognized in advance, and charging safety and equipment reliability are improved. According to data collected by a sensor network, such as charging state, short circuit fluctuation, leakage current and cable temperature, the system can automatically identify faults such as poor plug contact, micro short circuit, electric leakage and temperature abnormity, generate corresponding fault instructions and timely take measures such as power failure and alarm to prevent accidents. And the self-discharge monitoring function can also judge whether self-discharge abnormity exists in the battery according to battery discharge data, and timely notify a user to overhaul, so that the battery is prevented from being damaged.

The invention provides a pollution discharge permission evaluation and management method based on intelligent analysis, and relates to the technical field of pollution discharge management, and the method comprises the steps: obtaining first pollution discharge permission information of a first enterprise; generating first period enterprise pollution discharge data and first period enterprise energy consumption data; carrying out pollution discharge standard exceeding judgment to generate a first period judgment result; when the judgment result of the first period is that the standard does not exceed the standard, pollution discharge standard exceeding risk prediction and energy consumption prediction in a second period are carried out based on the enterprise pollution discharge data of the first period, the enterprise energy consumption data of the first period, the pollution discharge limit value information of the second period and the energy consumption limit value information of the second period, and a standard exceeding risk index is generated; and according to the standard-exceeding risk index, carrying out pollution discharge standard-exceeding reminding on the first enterprise. According to the method and the device, the problem that an early warning mechanism for future pollution discharge risks is lacked in the prior art can be solved, and the technical effects of timely supervision and emission standard exceeding reminding are achieved.

A method and apparatus for predicting the state of capacity of a battery, and a device, a medium and an energy storage system. The method comprises: acquiring first battery operating condition information (S101); on the basis of a model application condition that the first battery operating condition information meets, determining from a preset prediction model set a plurality of target prediction models which correspond to the model application condition (S102); using the plurality of target prediction models to predict the state of capacity of a battery on the basis of charge / discharge data of the battery, so as to obtain a plurality of corresponding prediction results (S103); and fusing the plurality of prediction results, so as to obtain a fused prediction result (S104). In the method, a plurality of suitable prediction models are selected on the basis of battery operating condition information to predict the state of capacity of a battery, and prediction results of the plurality of prediction models are fused, such that a finally obtained prediction result is relatively accurate.

According to the sewage discharge data management method based on the intelligent water affair, the sewage discharge data, the equipment state data and the environment data of an enterprise are subjected to edge calculation, the initial discharge data and the equipment evaluation data are generated, localized real-time processing of the data is achieved, and the transmission amount of invalid data is reduced. And performing data implicit anomaly identification and multi-modal data cleaning on the initial emission data and the equipment evaluation data based on the sewage component chemical association rule base, accurately capturing abnormal points which do not conform to chemical association rules in the data, and generating an abnormal mark list and intermediate emission data. Data association analysis is carried out based on the abnormal mark list, the intermediate emission data and enterprise operation data corresponding to the enterprise, a data logic verification map is constructed, errors in the intermediate emission data are automatically corrected according to a logic relation in the verification map, the processing efficiency is improved, and through cross verification of multiple data sources, the data processing efficiency is improved. And target emission data and a compliance report with high accuracy are generated.

The invention provides a building construction carbon emission real-time monitoring and intelligent analysis system based on the Internet of Things, relates to the technical field of carbon emission monitoring and analysis, and solves the problems of functional deficiency and imperfection of a carbon emission monitoring system. In the system, a sensing layer is used for collecting multi-dimensional carbon emission related original data of a construction site in real time and carrying out edge side preprocessing; the transmission layer is used for transmitting data by adopting a self-adaptive multi-mode communication protocol and adapting to the network environment change of a construction site; the platform layer is used for receiving and integrating data, executing carbon emission accounting, performing multi-dimensional intelligent analysis, constructing and updating a digital twinborn simulation model and driving an intelligent analysis process; and the application layer is used for visually and dynamically presenting system information data, providing interactive access and early warning, and opening key carbon emission data and supervision support to a government supervision platform. The method is optimized and improved from multiple aspects, so that the system has relatively high application efficiency and meets the requirements of domestic and overseas carbon auditing.

The invention discloses a method and a system for predicting a partial discharge source in a transformer based on a random forest. The method comprises the following steps: acquiring signal data acquired by an acoustic sensor from the partial discharge source in a transformer model; extracting acoustic partial discharge data from the data acquired by each acoustic sensor; and selecting a feature set from the acoustic partial discharge data, inputting the feature set into the random forest classifier for training and testing, and identifying the type of the partial discharge source. According to the method, acoustic partial discharge data in an acoustic sensor are extracted through time sequence features based on extensible hypothesis testing, the features are selected by using a mutual information method and then imported into a random forest classifier, and independent partial discharge and coupled partial discharge are identified through data sample training and testing. The method improves the recognition capability of the partial discharge source, improves the prediction range of the partial discharge source, and greatly reduces the cost and the time required for training the classifier.

The invention discloses a carbon emission auditing method and system based on a block chain technology, and relates to the technical field of block chains and carbon emission auditing, and the method comprises the steps: obtaining the carbon emission data of a target carbon emission entity; based on a preset carbon emission internal control standard, evaluating the integrated data; when the evaluation result indicates that the abnormal item exists, tracing the abnormal data by utilizing the traceability of the block chain technology; for the data passing the traceability verification, using an intelligent contract to verify whether the data meets a predetermined standard; and performing reason analysis on the screened abnormal data, and evaluating the potential influence of the abnormal data. According to the carbon emission auditing method based on the block chain technology provided by the invention, enterprises can record and share the carbon emission data in real time through full-process data right confirmation and tampering resistance of the block chain technology, and tedious links of data collection and verification in traditional auditing are reduced, so that the auditing cost is effectively reduced, and the auditing efficiency is improved. And the accuracy and credibility of carbon emission auditing are improved.

The invention discloses a reservoir carbon emission data analysis method based on a big data technology, and relates to the technical field of reservoir carbon emission data analysis, and the method comprises the following steps: obtaining reservoir carbon emission monitoring data through a collection device, preprocessing the reservoir carbon emission monitoring data, and calculating the vegetation coverage rate of a reservoir region; the method comprises the following steps: respectively acquiring influence indexes of each reservoir carbon emission monitoring data on carbon emission, constructing a reservoir carbon emission monitoring model by adopting a random forest algorithm, evaluating a reservoir carbon emission level, sending out a corresponding signal and transmitting the signal to a client, according to the method, the unmanned aerial vehicle and multispectral sensor collaborative acquisition technology, the random forest algorithm modeling technology, the multi-source data fusion analysis technology and the modern information technology are closely combined, the influence index of each reservoir carbon emission monitoring data on the carbon emission is accurately obtained, and the real-time and comprehensive monitoring of the reservoir carbon emission is achieved; and the intelligent degree in the reservoir carbon emission process is obviously enhanced.

The invention relates to a 10kV switch cabinet intelligent automatic operation monitoring and early warning method and system and a medium, and the method specifically comprises the following steps: 1, collecting the partial discharge data information of each key part, including a sound wave signal, an instantaneous pulse current signal and a high-frequency electromagnetic wave signal; 2, carrying out data processing, and obtaining a partial discharge abnormal point data value of each key part; 3, realizing partial discharge early warning of the switch cabinet by adopting a fault early warning method based on current and sound wave abnormal point data of the switch cabinet; 4, collecting the temperature of each key part and the environment temperature; 5, carrying out data processing, and obtaining a change relation function of the temperature rise of each key part along with the current; step 6, establishing a switch cabinet dynamic early warning model to realize dynamic temperature rise early warning; the method has the advantages that the operation and maintenance cost is reduced, the equipment operation efficiency is improved, potential safety hazards are found in time, and remote monitoring and early warning are achieved.

The application provides a kind of box-type substation monitoring method and device, system, storage medium, belong to substation monitoring technical field, this method includes: based on the discharge data of box-type substation that first ultrasonic wave data is corrected to the electromagnetic pulse data collected by electromagnetic sensor in box-type substation;Box-type substation discharge data is compared with preset threshold value, whether the fault of box-type substation occurs is judged;Wherein, first ultrasonic wave data is the data collected by ultrasonic sensor in different positions in box-type substation;In response to the fault of box-type substation, the fault position of box-type substation is determined based on discharge data using time difference positioning method;Based on fault position, the operation state data of box-type substation is analyzed to determine fault type, and the parameters of box-type substation are compensated based on the compensation mode corresponding to different fault types.The application adopts multi-modal compensation strategy based on fault type, to improve the power supply reliability of box-type substation.

The invention discloses an intelligent monitoring system for multi-source data fusion of a biomass intelligent power plant, relates to the technical field of intelligent monitoring, and solves the problems proposed in the background technology. The biomass multi-source data acquisition module is used for acquiring biomass fuel characteristic data, equipment operation data, environment-friendly emission data and raw material supply chain data in real time; the data preprocessing and order reduction module is used for carrying out denoising, normalization and coupling order reduction processing on the biomass fuel characteristic data and the equipment operation data; the multi-source data dynamic fusion module is used for carrying out weighted fusion on the multi-source data according to the fuel heat value, the water content and the environmental protection weight; the biomass combustion optimization diagnosis module is used for constructing a deep learning model based on the fusion data and outputting combustion efficiency optimization parameters and a fault diagnosis result; according to the method, multi-dimensional data of fuel characteristics, equipment operation, environmental protection emission and the like are integrated, through links of preprocessing, order reduction, fusion and the like, an information island phenomenon is eliminated, and scientificity and accuracy of decision making are improved.

The invention discloses a data verification method, device and equipment, a medium and a product. The method comprises the following steps: acquiring a data resource set and an equipment running state; on the basis of the pollution source pollution discharge detection data and the pollution source pollution discharge permission data, carrying out standard exceeding judgment on the discharge port concentration of the equipment to obtain a judgment result; if the judgment result indicates that the discharge port concentration of the equipment is normal, establishing a correlation matrix based on the pollution source power utilization detection data and the pollution source pollution discharge detection data, and determining a correlation coefficient corresponding to the correlation matrix; determining a response level corresponding to the correlation coefficient; and determining a data counterfeiting result according to the response level. By calculating the correlation coefficient between the pollution source electricity utilization detection data and the pollution source pollution discharge detection data, the pollution discharge data counterfeiting phenomenon of the industrial enterprise is effectively identified, and the coverage rate of illegal clues of the industrial enterprise is further improved.

The invention relates to the technical field of pollution source supervision, and discloses a pollution source off-site supervision method and system based on big data, and the method comprises the steps: building a dynamic verification rule library through coupling the operation beat data of enterprise key equipment and the total power consumption fingerprint data, and comparing the matching degree between the actual energy consumption and the expected mode in real time and generating a process credibility index. According to the invention, conversion from traditional pollution discharge data verification to production process stability verification is realized, and an unforgeable supervision trust mechanism is constructed through the physical relevance between the equipment start-stop signal and the power utilization characteristic; and meanwhile, the judgment accuracy under the extreme working condition is ensured by utilizing environmental parameter dynamic compensation and micro-differential pressure bypass verification, and finally a quantifiable accumulation enterprise environmental protection credit evaluation system is formed.

The invention discloses a method and a system for estimating the state of charge of a lead-acid storage battery, and relates to the technical field of batteries, standard discharge data and discharge data of the lead-acid storage battery are acquired, capacity prediction is performed on the standard discharge data and the discharge data by adopting a pre-trained discharge capacity detection model, and the state of charge of the lead-acid storage battery is estimated. And obtaining a discharge capacity value and a standard discharge capacity value, and carrying out charge state analysis on the discharge capacity value and the standard discharge capacity value to obtain a charge state estimation value corresponding to the lead-acid storage battery. The technical problem that in the prior art, the state of charge of the lead-acid storage battery is mainly predicted through a pre-trained feedforward neural network model, but most of discharge data in a project is incomplete discharge, so that the model is difficult to obtain an accurate estimation result is solved.

The invention relates to an environmental factor deduction carbon emission transaction method based on privacy protection, and belongs to the technical field of carbon emission data management. Comprising the steps of calculating and determining an effective result of the day based on observation data of each accounting subject, performing threshold signature and updating a global model; calculating the current-day voucher anchor hash; calculating a document deduction amount, a total voucher deduction amount and a final discharge amount by using the global model, generating a remote report and a corresponding standard hash, and outputting a three-item binding hash; constructing an aggregable zero-knowledge proof circuit, and jointly verifying a deduction equation, a membership relationship, uniqueness constraint and binding consistency in one proof; and verifying the threshold signature, the standard hash corresponding to the remote report, the three-item binding hash and the zero-knowledge proof in a single transaction to complete the transaction. According to the method, automation, credibility and traceability of the whole environmental factor deduction carbon emission transaction process based on privacy protection are realized, and end-to-end verifiable, low-cost and high-throughput carbon emission settlement is realized.

The invention discloses a two-dimensional code traceability assisted product carbon footprint tracking method, system and device, and relates to the technical field of carbon emission management. The method comprises the following steps: acquiring a full life cycle of a target product and creating a storage chain; generating and binding a unique two-dimensional code; a writing instruction is generated when the two-dimensional code is triggered; calling preposed carbon emission data, and reading environmental parameters of scanning nodes; performing evolutionary analysis in combination with the environmental parameters and the emission data to generate update data; associating the preposed data and then writing into the storage space; and performing carbon footprint tracking management through the two-dimensional code and the updated chain. The technical problems that in a traditional carbon footprint calculation method, due to data splitting, dynamic updating lagging and boundary definition fuzziness, carbon emission accounting is inaccurate, and tracking efficiency is low are solved, and a carbon footprint dynamic evolution mechanism combining a two-dimensional code identification binding period storage chain and environment context parameter real-time collection is adopted. The technical effect of improving the real-time performance, the accuracy and the traceability of the carbon footprint accounting data is achieved.

The invention discloses a carbon footprint monitoring system suitable for a high altitude area and an energy-saving data processing method, and the method comprises the steps: firstly, through an EnKF (Ensemble Kalman Filter) technology, fusing multi-source heterogeneous data such as satellite remote sensing and ground monitoring according to a data source reliability "matching", and overcoming the data sparsity; secondly, based on the oxygen partial pressure corresponding to the local altitude and the'ratio 'of a key sensitivity coefficient # imgabs0 #, applying a specific correction function to dynamically adjust an emission factor; and finally, downscaling the coarse-resolution carbon emission data by adopting an adaptive-resolution convolutional neural network to generate a high-precision and high-resolution carbon emission spatial distribution diagram.

The invention discloses an improvement potential quantification method for carbon emission in a mining life cycle process, and relates to the technical field of mine carbon emission management, and the method comprises the steps: collecting real-time sensor data, equipment operation logs, process parameters and unstructured documents of each stage in the full life cycle of a gold mine, and constructing a dynamically updated multi-source data pool; based on the multi-source data pool, carbon emission entities and a carbon emission entity semantic relationship are extracted, and a carbon footprint knowledge graph is constructed; performing multi-objective optimization on a collaborative decision result, solving a non-dominated solution set, screening an optimal equilibrium solution, and generating an optimization strategy sequence and corresponding expected carbon emission; and inputting the optimization strategy sequence into a mine production scheduling process for execution, monitoring actual carbon emission data in real time, comparing the actual carbon emission data with expected carbon emission, and correcting the dynamic carbon emission reference model through a comparison result. According to the method, the carbon footprint knowledge graph is constructed, so that the semantic understanding and correlation analysis of the whole-process carbon emission of the mine from exploration to pit closing are realized.