1485 results about "Calculated data" patented technology

The invention discloses a thunder and lightning monitoring and early warning method and system based on multi-source data fusion, and relates to the technical field of thunder and lightning early warning, and the method comprises the steps: extracting electric field time domain and frequency domain features, magnetic field change features, lightning activity modes and meteorological change features through obtaining atmospheric electric field, magnetic field, lightning activity and meteorological environment data in real time; and constructing multi-source feature data. A time sequence analysis and Bayesian fusion technology is adopted to calculate a correlation weight between data sources, and a fusion feature vector is generated. And establishing a weighted regression model based on the vector, calculating thunder and lightning occurrence probability through dynamic weight distribution, and generating a risk distribution map in combination with geographic information. The method has a closed-loop feedback optimization mechanism, model parameters and weights can be adaptively adjusted according to prediction errors and early warning accuracy, the accuracy, timeliness and environmental adaptability of lightning early warning are improved, and the method is widely applied to the fields of electric power, aviation, buildings and the like.

The invention relates to the technical field of network communication, and discloses a vehicle-mounted edge computing data recording system and method based on protocol adaptive analysis, and the system comprises a multi-protocol network access controller which is used for capturing an original data frame and extracting a physical feature triple; the identification analysis engine is used for executing hash operation on the triple to generate a physical feature code and retrieving a physical logic address mapping table; the direct memory access controller is used for responding to a target memory address pointer hit by retrieval and directly writing a data load into an input buffer area of the functional operation module, and by constructing a direct addressing mechanism based on Hash mapping, thorough decoupling of vehicle-mounted heterogeneous network physical topology and edge computing logic is achieved.

The invention specifically relates to the technical field of edge computing, and discloses a multi-platform concurrent transmission edge computing data acquisition method, which comprises the following steps: S1, concurrent access of multi-platform equipment: establishing a mapping relationship between equipment identity and a communication link; s2, data concurrent collection and preprocessing: obtaining a preprocessed data stream; s3, concurrent transmission priority scheduling: allocating a transmission bandwidth for each data stream; s4, edge node parallel computing: distributing the subtasks to a plurality of computing nodes; s5, cloud collaboration and global optimization: generating a global optimization strategy; s6, carrying out full-link monitoring and dynamic regulation and control; concurrent access of heterogeneous equipment is realized by generating data streams in a uniform format, multi-platform compatibility is effectively enhanced, bandwidth is dynamically allocated to the data streams according to priority weight indexes by calculating the priority weight indexes of the data streams, data transmission efficiency is improved, and the data transmission efficiency is improved by allocating sub-tasks to a plurality of computing nodes. And the resource utilization rate of the edge computing gateway is improved.

A method is provided for constructing decision trees. The method includes analyzing a dataset to determine data distribution metrics; calculating feature importance scores for features in the dataset; dynamically adjusting tree depth and node pruning criteria based on the determined data distribution metrics and feature importance scores; initializing a decision tree structure based on the adjusted tree depth and node pruning criteria; and iteratively refining the decision tree by applying heuristic adjustments to improve splits based on updated data distribution metrics and feature importance scores.

The invention discloses a data ownership verification and authorization control method based on zero knowledge proof. The method comprises the following steps: constructing a traceable Merk tree structure on a block chain or a distributed account book; a data owner calculates data fingerprints and packages the data fingerprints into declaration nodes to be inserted into the traceable Merk tree; storing the ownership zero knowledge proof and the traceable Merk tree root hash into a block chain together; the verification party executes the zero-knowledge verifier contract on the chain to complete ownership verification without reading original data or identity plaintext; access control is realized after on-chain verification of the service party; triggering local heavy hash to update the traceable Merk tree root hash, and broadcasting a revocation event on a chain; and confirming legality of evolution and authorization change of all nodes. According to the method, the historical traceability and structural consistency of the data ownership and authorization relationship are ensured, and efficient traceability and anomaly detection of the node evolution chain are also realized.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a model task processing acceleration method, device and equipment and a medium. The attention weight is obtained before a first preset processing layer, the importance score of the first type of data marks is calculated in the processing layer, a subset of the first type of data marks is screened out, then deep processing is executed on the subset and the second type of data marks, and an output sequence is generated. According to the method, the attention weight is extracted and the importance score of the data mark is calculated in the shallow layer stage, so that the redundant visual mark is effectively identified and eliminated, only necessary information is reserved for deep processing, and the invalid calculation overhead is avoided, and therefore, on the premise of not changing the structure and the performance of an original model, the accuracy of the model is improved. The model reasoning efficiency is effectively improved; and the resource consumption is reduced.

The invention relates to the technical field of power grid state monitoring, in particular to an intelligent monitoring and early warning method and system for a high-voltage power grid, and the method comprises the steps: collecting the multi-dimensional parameter data of a power grid node, and obtaining the multi-dimensional parameter data of the power grid node based on the relative deviation of the data of each dimension in a local window and the mean value of the data of each dimension in combination with the correlation coefficient of the data of each dimension; calculating parameter fluctuation attention at a target moment so as to correct parameter data of each dimension; processing the data points through a clustering algorithm to obtain a plurality of clusters, and selecting the cluster center of the cluster with the most data points as a power stability index; and calculating the relative deviation between the data point and the index, and generating a state early warning coefficient so as to estimate and evaluate the abnormality of the power grid node region and generate an early warning signal. According to the method, parameter fluctuation is accurately quantified by fusing the deviation degree and correlation of the multi-dimensional data of the local window, and a foundation is built for monitoring and early warning.

The invention provides a knowledge base and business system collaboration method under an AI platform, and belongs to the technical field of AI platform digital data processing.The method includes the steps that triple semantic analysis is conducted on query by constructing a multi-level semantic vector representation module, a parallel data retrieval engine is started, vector retrieval, graph reasoning and real-time data pulling are executed at the same time, and the query efficiency is improved; establishing a dynamic confidence evaluation mechanism to evaluate the quality of the data source, executing a weight distribution algorithm based on reinforcement learning, dynamically calculating the weight of the data source according to a query type by adopting a graph convolutional network intelligent routing decision model, and implementing multi-source data fusion and consistency verification to solve data conflicts through a weighted voting mechanism. An intelligent result sorting and filtering system is established, a multi-dimensional evaluation strategy is adopted to output high-quality answers, a continuous learning and feedback optimization loop is constructed, system performance is continuously optimized through active learning and a graph shortest path algorithm, and the technical problem that knowledge base data and a service system cannot effectively and uniformly make decisions is solved.

The invention relates to the technical field of data processing, and discloses an intelligent monitoring method and system for cyanobacterial bloom outbreak. The method comprises the following steps: collecting a water surface spectrum and underwater particle size data, carrying out atmospheric correction, calculating a normalized algae index and a blue-green wave band ratio, inputting the normalized algae index and the blue-green wave band ratio into a U-Net network to obtain a water bloom coverage area, carrying out integral interpolation on the particle size data to obtain a vertical section distribution curve, and calculating a surface layer enrichment degree and a floating trend index, and establishing a water surface-underwater association relationship through random forest regression training, and inputting the multi-dimensional features into a CNN-LSTM model to predict a water bloom outbreak probability and determine an early warning level. According to the method, the problem that the cyanobacterial bloom three-dimensional structure cannot be comprehensively described due to the lack of effective fusion of the water surface spectral data and the underwater vertical section data is solved, the problem that the early warning timeliness of cyanobacterial bloom outbreak is insufficient due to the lack of a multi-source data time sequence analysis model is solved, and the spatial integrity and early warning advance of cyanobacterial bloom monitoring are improved.

The invention provides an automatic data management method and system based on a multi-modal large model, and the method comprises the steps: collecting multi-source heterogeneous industrial data, and carrying out the standardization processing, and forming standardized multivariable time series data; constructing a process knowledge base, and performing semantic embedding coding on a process knowledge text and storing the process knowledge text; constructing and finely tuning a KTSF multi-modal large model, and fusing process knowledge semantics and multivariable time sequence data through a cross-modal attention mechanism to generate joint semantic representation; based on prediction of a KTSF multi-mode large model, outputting a residual error with actual data, and dynamically identifying abnormal data; performing attribution analysis; based on an attribution result, calling a KTSF multi-mode large model to generate a repair value, and performing intelligent correction on the abnormal data; the design quality evaluation and feedback learning module is used for calculating a data quality score and driving incremental updating of the model; and the design rule self-learning module is used for automatically extracting the governance rule through clustering analysis and updating the knowledge base.

The invention is suitable for the technical field of 3D scanning, and provides a figure model 3D scanning and dynamic attitude capturing method and system, which dynamically triggers 3D scanning by monitoring joint movement intensity in real time, continuously collects continuous movement tracks by using an inertial sensor in a non-scanning period, and reversely deduces space-time parameters of a shielded part through multi-light-source shadow boundary evolution. And constructing a spatio-temporal joint evaluation function to adaptively adjust the scanning frequency and the sampling rate, and finally performing spatio-temporal alignment and confidence weighted fusion on the discrete point cloud, the continuous trajectory and the reverse reckoning data to generate a dynamic biomechanical model. The scheme breaks through the bottleneck of resource waste and blind area shielding of traditional fixed frequency scanning, realizes space-time complementation and precision optimization of motion monitoring, and is suitable for dynamic scenes such as movie and television animation, medical rehabilitation, physical training and the like. According to the system, through multi-modal data fusion and self-adaptive resource scheduling, the data integrity, the real-time performance and the biomechanical analysis capability are remarkably improved, and an efficient solution is provided for human motion digitization.

The invention discloses a model concept drift detection and adaptive learning method and system, and the method comprises the steps: obtaining a millimeter wave image collected by a millimeter wave sensor, and carrying out the prediction of the millimeter wave image through a pre-constructed neural network model; performing concept drift detection on the neural network model by taking the millimeter wave image and the corresponding model prediction result as data samples; the method specifically comprises the steps of calculating feature distribution of a data sample, and judging whether the feature distribution drifts or not; marking the data sample, calculating a model performance index of the marked data sample, and judging whether the model performance index drifts or not; and when drift triggering is detected, online incremental learning and model adaptive updating are carried out on the neural network model. Through the model concept drift detection and adaptive learning method, the generalization ability and robustness of the AI model can be greatly improved, so that the detection accuracy is remarkably improved, and the misjudgment rate is reduced.

The invention relates to the field of edge computing data security, in particular to an integrity auditing method and system in an edge computing environment. The problem that an existing cloud auditing model is high in delay and large in bandwidth consumption in an edge scene is mainly solved. A lightweight audit challenge is generated through random sampling, data block aggregation operation is executed at an edge node, Merkel Hash tree path evidence is extracted, and integrity proof of dual verification is formed; and the aggregated evidence and the path evidence are verified by using bilinear mapping, so that efficient and credible auditing is realized. Dynamic data updating and accurate positioning of damaged blocks are supported, communication overhead is remarkably reduced, and real-time credibility of edge data is guaranteed.

The invention discloses a regional waterlogging early warning method and device based on a multi-water professional model and a medium, and belongs to the technical field of hydrodynamic models. The method comprises the following steps: selecting a research area, and collecting meteorological data, geographic data and hydrological data of the research area; constructing four basic models of the research area based on the data, and coupling the four basic models to construct a waterlogging forecast simulation overall model; importing an input value of historical measured data of the research area into the waterlogging forecasting simulation overall model to output calculation data, and determining parameters of the waterlogging forecasting simulation overall model when the deviation between the calculation data and the output value of the historical measured data meets the requirement; and based on preset rainfall data, performing regional waterlogging forecasting simulation through the waterlogging forecasting simulation overall model, and outputting a drainage pipe network water flow condition, an open channel water flow condition and a waterlogging ponding submerging condition. According to the method, the multi-water professional model is integrated, and the technical effect of co-simulation of the waterlogging evolution process is achieved.

The invention relates to the technical field of multimodal transport, in particular to a multimodal transport one-box system intelligent credible collaboration method based on an artificial intelligence technology, comprising the following steps: S1, obtaining a standardized JSON data packet from transport data according to an agreed rule, performing triple encryption to obtain an encrypted data packet, and broadcasting the encrypted data packet to a block chain; s2, the verification node carries out consensus verification on the Internet of Things equipment data and the electronic waybill key field, and if the verification is passed, the verification is written into the block chain; s3, the digital twin system fuses the multi-source data and extracts calculation data, and artificial intelligence calculates an optimal loading and unloading sequence and a transportation path according to the calculation data; and S4, uploading the longitude and latitude data to the block chain node through the Beidou positioning terminal and the 5G / LoRa gateway along the way, and automatically identifying the abnormity in the transportation stage based on the geofence technology. According to the method, data rules are unified, efficient connection of cross-transportation modes is achieved, manual input errors or violation operation is prevented, the risk of goods damage is reduced through dynamic route planning, the transfer efficiency is improved, and logistics time and cost consumption are reduced.

The invention discloses a storage and calculation separation method and device based on multi-level cache and intelligent scheduling and a server, and belongs to the technical field of data processing, and the method comprises the following steps: collecting operation behavior data of a user, carrying out distributed storage, and dynamically distributing and calculating node cache space; performing classification marking to form marked user data; performing intelligent scheduling layer analysis tasks on the marked user data, and screening and distributing adaptive computing nodes; the computing node receives the analyzed task to check the local cache, obtains corresponding data from the local or request remote storage according to the validity of the local corresponding data, computes the corresponding data, and caches the computed data to the local or remote storage according to the user access probability; and carrying out dynamic scheduling and hierarchical storage on the calculated data between local cache and remote storage according to access frequency and heat factors. According to the invention, the problems of low transmission efficiency and unbalanced resource utilization of the existing storage and calculation separation architecture are solved.

The invention belongs to the technical field of artificial intelligence, is applied to the field of financial science and technology and the field of medical health, and discloses a rule engine configuration method, device and equipment based on machine learning and a storage medium. The method comprises the steps that historical calculation data and performance indexes in historical calculation tasks are collected, and a training data set is constructed; a machine learning model is trained, and algorithm parameters of the rule engine are dynamically optimized through the training data set; configuring an expression and a user-defined function of a rule engine, and realizing analysis and execution of scripts through JEXL (JavaScript Exchange Library); establishing a dynamic mapping relationship between the algorithm configuration library and the business variables, mapping algorithm variable names and business variable names, and generating configurable algorithm rules; and analyzing task load and resource requirements based on a pre-trained machine learning model, distributing system resources, and repairing abnormal behaviors in a calculation process. According to the invention, the problems of low processing efficiency, high maintenance cost, unreasonable resource allocation and poor expandability in the prior art are solved.

The invention belongs to the technical field of artificial intelligence, and discloses an artificial intelligence model training resource adaptive distribution system. The method comprises the following steps: acquiring and calculating graph structure data and hardware resource state data in real time, and calculating a data reuse rate and generating a candidate operator fusion scheme by constructing an operator execution time sequence constraint matrix and identifying an operator cluster of data locality characteristics; a resource competition hotspot prediction mechanism is introduced, memory bandwidth occupation fluctuation characteristics are analyzed, a resource conflict probability is calculated for a fusion scheme, and a dynamic balance optimization model of fusion income and resource conflicts is constructed. An optimal operator fusion decision sequence and a resource allocation strategy are generated through iterative solution, and accurate dynamic adjustment of computing resources in the training process is achieved. The training efficiency and the resource utilization rate are improved, the energy consumption is reduced, and the system stability is enhanced.

The invention relates to the field of cold and hot data storage, in particular to a cold and hot data storage optimization method based on a large model, which comprises a business integration module, a data dependence module, a mode updating module, a cold and hot partitioning module and a file merging module, and is characterized in that the business integration module is used for collecting business data to form a training data set; the data dependency module is used for fitting business data to obtain a data consanguinity model, the mode updating module is used for predicting an access mode, the cold and hot partitioning module is used for calculating the heat of the data and performing dynamic scheduling, and the file merging module is used for automatically merging small files. A data storage structure is standardized, storage space is reduced, more efficient and more accurate cold and hot data classification can be achieved, the resource utilization rate of data storage hardware is improved, hot data response delay is reduced, the system storage utilization rate is remarkably improved, and the storage efficiency is improved.

The invention discloses an artificial intelligence auxiliary authentication method and system for data element right confirmation, and relates to the technical field of data element management and credible circulation. Comprising the following steps: data acquisition: acquiring a data product, an ownership statement and contract terms, and calculating an initial digital fingerprint of the data product; and ownership identification: extracting core value elements and features of the data product based on a multi-modal intelligent analysis model, quantifying value contribution weights formed by all links to the data product, and automatically generating a data element circulation graph. According to the method, the multi-modal intelligent analysis model is constructed, and the data product is deeply scanned and analyzed automatically, so that the dependence on predefined rules is reduced, and the intellectualization and automation of ownership identification are realized; by constructing an intelligent compliance verification model, data operation behaviors are monitored and compared in real time, substantive compliance control over the whole data use process is achieved, and a credible use closed loop is established.

A least-squares reverse time migration method, system, terminal, and non-transitory computer-readable storage medium. The method includes: establishing a modeling operator based on the scattering potential, construct an approximate asymptotic inverse operator for the reflectivity function, build a relationship between the reflectivity function and the scattering potential, and obtain an approximate asymptotic inverse operator for the scattering potential based on the approximate asymptotic inverse operator for reflectivity and the relationship; acquiring seismic data collected by receivers as observed data, and performing imaging based on the approximate asymptotic inverse operator and the observed data to obtain an initial image; generating predicted data based on the initial image and the modeling operator, and calculating data residual between the observed data and the predicted data; iteratively updating the initial image based on the data residual and the approximate asymptotic inverse operator to obtain a target image. The imaging efficiency is improved.

The invention discloses a multispectral and hyperspectral imaging proportion balance optimization method based on satellite remote sensing, and relates to the technical field of balance optimization, and the method comprises the following steps: collecting a first image data set which comprises a low-resolution multispectral image and a hyperspectral image; preprocessing the first image data set, screening out a low-resolution multispectral image from the preprocessed images, improving the spatial resolution of the multispectral image by adopting a super-resolution reconstruction algorithm, and generating a reconstructed multispectral image; fusing the reconstructed multispectral image and the preprocessed hyperspectral image, and performing optimization processing on a fusion result to generate an image with an optimal resolution; and extracting second image data based on the optimal resolution image, and establishing a proportion balance optimization model. According to the method, the data processing time and the imaging cost are accurately calculated, and the image fusion quality evaluation is combined, so that the optimal allocation of the imaging ratio under limited resources is realized.

The invention relates to the technical field of intelligent control, in particular to a coke oven gas collecting tube pressure dynamic adjusting method and system based on intelligent control, and the method comprises the steps: obtaining pressure data from a gas collecting tube through a sensor, and storing the pressure data in a time sequence format to obtain a pressure deviation initial sequence; according to the pressure deviation initial sequence, calculating a data difference value and carrying out smoothing processing to obtain a change rate sequence; analyzing the change trend of the change rate sequence, and calculating a secondary difference value to obtain a change acceleration sequence; according to the change rate sequence and the change acceleration sequence, predicting a pressure deviation development trajectory by adopting a filtering method to obtain a predicted deviation trajectory; and if the predicted deviation trajectory exceeds a preset threshold range, obtaining a deviation influence evaluation value through historical data comparative analysis. The problem that the pressure control precision of the coke oven gas collecting pipe is poor in the high-temperature and high-pressure environment is solved, and the pressure adjusting precision of the gas collecting pipe in the high-temperature and high-pressure environment is improved.

The invention discloses a customer information storage management method and system based on a block chain, and relates to the technical field of distributed data evidence storage, and the method comprises the steps: collecting terminal customer information, generating an encrypted data packet, carrying out erasure coding, and generating an encrypted data fragment set; carrying out distributed storage based on the encrypted data fragment set, and calculating a data availability commitment at the same time; executing availability sampling according to the data availability commitment, and generating a fragment validity proof; based on the fragment validity proof, performing format and validity verification to obtain a to-be-processed proof set; and generating a recursive aggregation proof according to the to-be-processed proof set, and submitting the recursive aggregation proof to the block chain to verify and update a proof storage state. And cryptographic authentication is carried out on the persistent evidence storage state of the data assets, so that the dual beneficial effects of ensuring data privacy and credible evidence storage are achieved.

The invention discloses a high-order direct-current micro-grid system multi-loss synchronous convergence modeling method based on an improved physical information neural network. The method comprises the steps that S1, a physical information neural network model is initialized; s2, calculating physical loss and updating parameters of the physical information neural network model; s3, reflecting parameters of the physical information neural network model around physical optimization result points; s4, calculating data loss by using the reflected parameters and updating parameters of the physical information neural network model; s5, using the parameters obtained in S2 and S4 to update the parameters of the physical information neural network model through the iterative operation of Douglas-Rachford splitting; and S6, iterating the parameter updating process to solve the physical information neural network model. Different from a traditional physical information neural network which is difficult to converge for high-order system training, the method can realize synchronous convergence of physical loss and data loss in high-order direct-current micro-grid system fitting, and has important significance for mastering an internal operation rule of a direct-current micro-grid system in real time and establishing a physically interpretable data driving model.

The invention discloses a U.2 interface-based server intelligent integrated hierarchical storage system and method, relates to the technical field of computer server storage, and is used for solving the problem of optimization of multi-type SSD data access efficiency and storage resource utilization rate. Firstly, a multi-protocol parallel channel is established by redefining U.2 interface reserved pins, signal integrity parameters are monitored in real time and fused with equipment I / O access features, and an I / O feature sequence of physical layer state information is generated; then, based on a time sequence convolutional neural network, extracting access mode multi-scale features, calculating a data block access heat probability, mapping and determining a target storage hierarchy, and generating a scheduling instruction containing a source physical address, the target hierarchy and a migration priority; the migration execution module realizes zero-copy migration of the data block between the shared DRAM pool and the SSD according to the instruction; and the resource coordination module monitors system load in real time, dynamically adjusts migration concurrency and an I / O sampling strategy, and realizes self-adaptive optimization of storage performance and resource utilization.

The invention relates to the technical field of geotechnical engineering investigation and artificial intelligence crossing, in particular to a marine fine-grained soil content prediction method based on multi-source CPTU data and machine learning, comprising: acquiring CPTU data and corresponding geotechnical test component content data; constructing a CPTU prototype vector of the geotechnical test point, and calculating a cosine similarity weight of the data point and the prototype vector and a Gaussian distance weight of the data point and a window center; generating a plurality of sliding windows by taking each test point as a center, and extracting a high-dimensional feature vector by using double weights; training a random forest regression model by using the feature vectors; moving a window of a to-be-predicted drill hole according to the step length, extracting features, inputting the features into the trained model, and generating a continuous depth profile of the fine-grained soil component content. According to the method, an indirect qualitative judgment mode of a traditional experience method is broken through, direct, quantitative and continuous prediction from CPTU data to the soil body component content is achieved, and the technical problems that a traditional method is high in cost, long in period, discrete in data and incapable of achieving quantification are solved.

The invention discloses a data transaction security assessment method and system, and relates to the technical field of data security and intelligent assessment. The method comprises the steps of performing feature analysis on a data field, determining a sensitivity weight and generating a sensitivity vector; the fields are graded according to the sensitivity vectors, and dynamic adjustment is carried out in combination with transaction related information to obtain a grading result; calculating a data content risk, a field association risk, and a transaction environment risk based on the sensitivity feature set; determining a weight according to a transaction scene, and weighting various risks to obtain a comprehensive score; and judging the risk level according to the comprehensive score and a threshold value dynamically determined based on historical data. According to the invention, through hierarchical sensitivity modeling and dynamic risk calculation, fine-grained security assessment of different types and sensitivity data is realized, and the risk result can be adaptively adjusted along with changes of data contents, transmission paths and transaction environments, so that the accuracy, real-time performance and scene adaptability of assessment are improved, and the risk assessment efficiency is improved. The defects of a static evaluation method in the prior art are overcome.

The invention relates to the technical field of smart park data monitoring, and discloses an artificial intelligence smart park multi-source data monitoring and analysis method. The method comprises the following steps: constructing a park operation state basic model; when the model is identified to be abnormal, data dynamic change characteristics are extracted by utilizing a spatio-temporal data fusion technology, and an abnormal event mode in a specific region is identified by combining deep belief network deep analysis; calculating data complexity by using permutation entropy, optimizing and analyzing an abnormal propagation path in combination with an A star algorithm, and evaluating a diffusion range and an influence path; recognizing a comprehensive risk area, analyzing the environment and equipment state characteristics of the area through a multispectral imaging technology, and evaluating the operation abnormity in combination with real-time multi-source data; and performing accurate intervention on the comprehensive risk area based on the abnormal condition. According to the method, effective integration and analysis of multi-source data of the smart park are realized, abnormity is accurately identified, risks are mastered, a scientific means is provided for park management, and stable and efficient operation of the park is guaranteed.

The invention provides a vehicle NVH comfort assessment method and device based on sound and vibration fusion and electronic equipment. The method comprises the following steps: performing in-vehicle interference noise identification processing on window-level data in a candidate window set to obtain a candidate effective window set; calculating a sound and vibration consistency index between the in-vehicle audio signal and the in-vehicle vertical acceleration signal for the window-level data in the candidate effective window set, and screening an external noise dominant window to obtain an effective window set; respectively calculating an acoustic index and a vibration index based on the effective window set, and performing calibration compensation on the acoustic index and the vibration index based on the sensor calibration information; and performing fusion calculation based on the data volume information of the effective window set, the working condition coverage information, the uncertainty information corresponding to the sensor calibration information and the proportion information of the effective window set in the candidate window set to obtain a comprehensive confidence index. According to the method, the evaluation accuracy and stability can be improved, the cross-equipment comparability is enhanced, and the result credibility is improved.