314 results about "Discharge data" patented technology

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 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 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.

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 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.

The invention discloses an enterprise greenhouse gas emission factor adaptive generation system based on multi-source data intelligent fusion, belongs to the technical field of environmental protection, and aims to solve the problems of fixed limitation of an emission factor model and difficulty in multi-source data integration. S2, intelligently fusing multi-source data; s3, establishing a dynamic emission factor model; s4, performing real-time monitoring and adaptive adjustment; s5, emission factors are generated and optimized; s6, emission factor application and decision support; s7, system integration and implementation; s8, continuously optimizing and learning; by adopting the dynamic emission factor model, the calculation formula of the emission factors can be adjusted in real time according to changes of production parameters and environmental conditions, the accuracy and timeliness of the emission factors are effectively improved, the emission data can reflect dynamic changes in the production process in time, and the hysteresis problem in a traditional method is avoided.

The invention provides a power plant carbon emission data full life cycle management method and device, and relates to the technical field of data processing, and the method comprises the steps: collecting production, fuel and coal quality test system multi-source data in real time through a standard interface, and forming a dynamic data flow; predicting the carbon content of the fire coal element based on a BP neural network, and calculating the carbon emission by combining an emission factor method; carrying out back calculation on the discharge amount by using a mass balance method, analyzing and verifying the relevance between data and an accounting result through multiple regression, and triggering correction when an error exceeds a threshold value; a double-stage attention deep learning network is adopted to predict the carbon price in a rolling mode, a carbon asset dynamic analysis result is generated in combination with a quota estimation model, real-time emission data, quota surplus and shortage and the carbon price trend are displayed through a visual interface, and graded early warning is conducted on abnormal data. According to the method, full-life-cycle automatic management is realized, the data acquisition real-time performance and accounting precision are improved, the data authenticity and traceability are guaranteed, and the carbon asset management decision support capability is optimized.

The invention discloses a power battery abnormal discharge early warning identification method and device. The method comprises the following steps: extracting non-charging data of each vehicle; carrying out discharge section division on the non-charging data based on a first set condition; calculating an abnormal discharge characteristic parameter of each discharge section based on the non-charging data of each discharge section; constructing a single-batch vehicle discharge statistical general table based on the abnormal discharge characteristic parameters of each discharge section and the discharge data at the starting and ending moments; performing power battery abnormal discharge early warning identification based on the single-batch vehicle discharge statistical general table and the early warning condition to obtain a discharge early warning record; and generating a single-batch early warning result list based on each discharge early warning record, and determining a risk level of the discharge early warning record based on the abnormal discharge characteristic parameters. According to the method, the long-time small-current abnormal discharge working condition can be accurately identified, battery over-discharge and safety accidents are avoided in advance, reliable guarantee is provided for power battery safety monitoring, and the intelligent level of new energy automobile battery management is improved.

The invention discloses a water pollution traceability analysis and ecological restoration decision support system. The system comprises a data acquisition layer, a data management and fusion layer, a pollution traceability analysis layer, an ecological restoration decision layer and a user interaction and visualization layer. The system collects multi-source water quality, hydrology and discharge data by deploying a distributed water quality sensing network, remote sensing and unmanned aerial vehicle monitoring equipment and a pollution discharge source online access module; through data cleaning, fusion and knowledge modeling, a pollution propagation model and a traceability inversion algorithm are constructed, and a pollution source and a contribution rate are identified; and simulating a multi-class repair measure response process in the digital twin water environment, and automatically recommending an optimal repair scheme in combination with a multi-objective optimization algorithm. The method has the advantages of accurate traceability, scientific decision, eco-friendliness, dynamic optimization and the like, and is suitable for pollution treatment and ecological restoration scenes of various water bodies such as drainage basins, lakes, urban rivers and the like.

The invention discloses a multi-parameter linkage water quality sampling method and system, and relates to the field of water quality sampling, and the method comprises the steps: obtaining hydrological data, main transportation path data and pollutant discharge data; setting water area sampling points of the target monitoring water area according to the hydrological data, the pollutant emission data and the main transportation path data; water quality sampling is conducted on the water area surface sampling points and the water area vertical sampling points; verifying the validity of the sampling point data, and determining the validity of the water area surface sampling points and the water area vertical sampling points according to the validity verification result; acquiring water quality monitoring data of the water area surface sampling points and the water area vertical sampling points; calculating a pollution source linkage index according to the water quality monitoring data, and determining a pollution emission event type; determining a time sequence phase definition for the pollution event based on the pollution emission event type and the water quality monitoring data; and determining the corresponding water quality sampling frequency and water quality sampling volume by combining with the time sequence stage definition. The accuracy of water quality monitoring can be effectively improved.

The invention discloses a mine ecological restoration effect evaluation method based on a life cycle evaluation model, and relates to the technical field of ecological restoration effect evaluation, and the method comprises the steps: defining a mine restoration effect evaluation target, an evaluation range and a function unit, and carrying out the evaluation of the mine ecological restoration effect based on the evaluation range and the function unit; collecting resource consumption data, environmental emission data and ecological state data in the restoration process, and constructing a dynamic life cycle list database; and carrying out characterization calculation on data in the dynamic life cycle list database to obtain a preliminary environmental influence index value, establishing a recognition rule of a life cycle evaluation model based on sensitivity analysis of the soil microbial community in the mine restoration area to heavy metal stress, and recognizing the ecological toxicity potential of fresh water from the preliminary environmental influence index value. According to the method, the decision support report is generated visually, and multi-dimensional, dynamic and accurate evaluation of the mine restoration effect is realized.

The invention relates to the technical field of power equipment, in particular to a high-voltage cable fault diagnosis method and system. The method comprises the following steps: collecting discharge data in a discharge process of a to-be-detected high-voltage cable, and analyzing fault trend data of the high-voltage cable in a target interval to obtain a trend prediction waveform; the method comprises the following steps: constructing a reference fault-sample waveform signal mapping library through historical discharge data of a to-be-detected high-voltage cable, selecting at least one reference fault from the reference fault-sample waveform signal mapping library to form a fault combination, and superposing sample waveform signals corresponding to the reference faults in the fault combination to obtain a sample waveform signal; obtaining a sample comprehensive signal corresponding to the fault combination; and predicting a potential fault and a possible occurrence time point of the high-voltage cable based on the sample comprehensive signal and the corresponding fault combination. According to the invention, before the fault occurs, the possible fault can be checked in time, and targeted advanced processing is carried out.

The invention discloses a carbon emission prediction method and device based on iTransform, and the method comprises the steps: constructing an improved data preprocessing network based on data features, and carrying out the preprocessing of carbon emission data through the data preprocessing network; on the basis of the data preprocessing network, in combination with multivariable and non-stationary data features of the data preprocessing network, selecting an adaptive converter architecture, integrating a wavelet convolution module for capturing multi-scale features of data, and introducing a frequency enhancement channel attention mechanism to serve as an improved iTransform prediction network; inputting the preprocessed carbon emission data into an improved iTransform prediction network, and outputting a carbon emission prediction value at a future moment; pollution prevention and control are assisted based on the carbon emission prediction value, and environmental pollution is reduced. The device comprises a processor and a memory. According to the method, the accuracy of carbon emission content prediction in the future and the precise CO2 emission prediction capability are guaranteed, early warning and regulation prompts can be provided for related industries, emission reduction of each industry is supported, pollution prevention and control are assisted, then environment improvement is promoted, and the pressure of carbon emission on an ecological system is relieved.

The invention discloses a super capacitor monomer health state estimation method based on charging and discharging data, which belongs to the technical field of measurement, and comprises the following steps: carrying out de-noising processing on circulating charging and discharging data of a tested super capacitor monomer, calculating instantaneous frequency and instantaneous energy of the charging and discharging data after de-noising; energy feature distribution of time domain and frequency domain decoupling of the de-noised charging and discharging data is obtained, an energy feature distribution decomposition result is synchronously input into the health state estimation model based on the convolutional neural network, and the real-time capacity and the maximum capacity of the measured super capacitor monomer are fed back to the health state estimation model. And obtaining a predicted value of the capacity of the tested super capacitor monomer after training, and estimating the health degree of the super capacitor monomer. According to the method, collaborative analysis is carried out by fusing the time-domain dynamic characteristics and the frequency-domain steady-state characteristics of the charging and discharging data of the supercapacitor monomer, the limitation of traditional single-dimensional data representation can be broken through, and the detection sensitivity of the degradation phenomenon of the lithium ion supercapacitor is improved.

The invention discloses an iron and steel enterprise multi-process carbon emission on-line monitoring and early warning platform based on the Internet of Things, and relates to the technical field of carbon emission abnormity monitoring and alarming in the industrial production process, and the platform comprises a sensor network which is disposed at key equipment nodes of iron making, steel making and steel rolling processes, and is used for collecting carbon emission data and process parameters in real time; and the edge computing node is in communication connection with the sensor network and is used for carrying out filtering and time alignment processing on the acquired data. According to the iron and steel enterprise multi-process carbon emission online monitoring and early warning platform based on the Internet of Things, the problems of false alarm and missing alarm caused by the characteristic difference of iron and steel multi-process carbon emission are fundamentally solved through process exclusive carbon behavior map modeling and dynamic threshold generation mechanism; the stepped threshold value of the electric furnace process covers the power transition emission fluctuation, and the converter oxygen blowing intensity and the floating binding of the threshold value upper limit avoid the instantaneous peak value false alarm.

The invention relates to the technical field of chemical environment assessment, and discloses a regional influence assessment method for chemical sewage discharge. The method comprises the following steps: acquiring historical discharge data and regional environment monitoring data of a chemical sewage discharge source, generating a historical influence data set, extracting characteristic parameters, and establishing a trend change characteristic matrix; a multi-dimensional influence space is constructed, and a feature early warning index set is determined by calculating the state aggregation degree of historical pollution events; a regional environment digital twinborn model is constructed, real-time emission and environment monitoring data collected by a sensor are synchronized, an environment state is simulated in the model, and potential influences are predicted. Extracting real-time characteristic parameters according to the real-time emission data, establishing a state vector, calculating spatial position correlation with the characteristic early warning index set, and determining a real-time risk correlation factor; and combining the historical characteristic parameter set and the trend change characteristic matrix to generate an influence probability prediction model, predicting a regional influence probability, and generating a graded early warning signal when the regional influence probability exceeds a threshold value.

The invention relates to a marine ship carbon emission accounting method and device based on interpretable residual learning, and a medium, and the method comprises the steps: complementing the missing of static parameters through ship IMO numbers, carrying out the unified coding and unit standardization of marine log dynamic data, combining the host power and oil consumption dual-path verification of the data validity, and carrying out the calculation of the marine ship carbon emission. After abnormity is cleaned, dynamic, static and meteorological data are fused to construct a carbon emission database; calculating the comprehensive action of wind resistance, still water resistance and wave resistance by applying a fluid mechanics law, generating a physically-driven carbon emission reference value, and synchronously extracting key characteristics such as navigational speed, wave height and ship length; with reference values and features as samples, uncovered dynamic features are learned through a multi-layer residual block chain structure, and a carbon emission accounting model is trained in combination with a cross validation and regularization mechanism; and finally realizing high-precision carbon emission prediction based on the training model. Compared with the prior art, the method has the advantages that the residual network is comprehensively used, interpretability analysis is achieved, and high-precision prediction of ship carbon emission is achieved.