634 results about "Compress Algorithm" patented technology

The present invention relates to the technical field of data transmission. Disclosed is an application-driven three-dimensional spatial data transmission method. The method comprises: determining system key performance indicators (KPIs) by means of qualitative and quantitative analysis; constructing an AI-driven adaptive three-dimensional data transmission mechanism, and dynamically adjusting a transmission strategy on the basis of a real-time network state, a device capability, an application scenario and the KPIs; developing an adaptive compression algorithm set oriented to three-dimensional data, so as to meet differentiated compression requirements of different application scenarios; performing loop execution of a test, and adjusting and optimizing the data transmission mechanism and the compression algorithm set on the basis of a test feedback result and the real-time network state; and deploying an optimized transmission method to a production environment, and collecting field data to optimize the system performance and verify the achievement of the KPIs. By constructing a qualitative and quantitative analysis framework based on machine learning, the present invention quantifies differentiated transmission requirements of different application scenarios and formulates transmission strategies meeting the scenario requirements.

The invention relates to the technical field of data monitoring, in particular to a complex equipment data monitoring method and system based on a knowledge graph and edge calculation. The method comprises the steps of multi-modal data acquisition and edge intelligent preprocessing; performing feature extraction on the preprocessed data; fusing multi-source heterogeneous data and constructing a dynamic knowledge graph; performing deep knowledge reasoning and layered fault early warning decision making; and performing self-optimization and knowledge closed-loop management based on early warning decision. According to the differential coding and self-adaptive compression algorithm of the edge layer, the data transmission quantity is reduced by 75%, the network bandwidth requirement is greatly reduced, and the system operation efficiency is improved.

The invention discloses a multi-source three-dimensional model lightweight processing and lossless compression supporting method, system and device and a storage medium, and relates to the three-dimensional technical field, and the method comprises the following steps: carrying out format compatibility check on an imported three-dimensional model, and analyzing the geometric structure, texture characteristics and material attributes of the model; the geometric structure of the three-dimensional model is simplified by adopting a quadratic error measurement algorithm and edge folding, the simplified model is optimized through edge segmentation operation, and a lightweight model is obtained; and compressing geometric data of the lightweight model by using a data compression algorithm, and compressing texture data through predictive coding to complete lossless compression of the three-dimensional model. According to the method, the loading speed and the real-time rendering efficiency are remarkably improved by reducing the size of the three-dimensional model file and optimizing the texture compression algorithm. The lossless compression technique ensures that a high-quality visual effect is kept in the lightweight process, the lightweight model reduces the storage space requirement, and the energy consumption of data transmission and processing is reduced.

The embodiment of the invention provides a kafka storage and calculation separated data processing method and device, and the method comprises the steps: constructing a multi-layer data processing architecture which comprises four layers: network communication, task distribution, processing scheduling and storage. Cold and hot data separation is realized through hierarchical design of a log pre-writing area and a main storage area, and intelligent scheduling of system resources is realized in combination with a copy manager, a coordinator and a load predictor. An adaptive compression algorithm and a verification mechanism are adopted to ensure the data transmission efficiency and integrity, a message cache region is created to store hotspot data, and flexible access of cold and hot data is realized based on message sites. According to the method, the defects of the traditional technology in storage and calculation coupling, storage efficiency, load balancing and the like are effectively overcome, and the performance and expandability of a message queue system are remarkably improved.

The invention discloses a cloud data anomaly detection and safety response system based on artificial intelligence, relates to the technical field of data processing, and solves the problems that firstly, an incremental compression algorithm is difficult to store and preprocess multi-source heterogeneous data; secondly, it is difficult to fuse statistical analysis and a deep learning model, locate outliers and analyze abnormal semantics in unstructured data, and then it is difficult to effectively predict a potential attack path; then, on the premise that data security and traceability are guaranteed, correlation analysis of cross-node anomalies is difficult to achieve so as to identify distributed attacks; and finally, an attack and defense confrontation model is difficult to construct for safety response, and the safety response effect is difficult to evaluate. According to the method, cloud environment data are processed through an adaptive probe cluster and the like, multiple models are fused to generate anomaly detection features and predict attack paths, and response and evaluation are carried out through reinforcement learning and digital twinning by means of cooperative detection such as federated learning and the like.

The invention relates to the technical field of big data processing, and provides a mass data storage optimization method which comprises the following steps: analyzing target data according to access frequency and / or access time of the data, and dividing the target data into hot data, warm data and cold data; aiming at the hot data, the temperature data and the cold data, performing compression processing by adopting corresponding compression algorithms; before the target data is stored, performing duplicate removal on the target data according to the fingerprint of the target data; monitoring the use condition of the memory, the access frequency of the target data and the performance of the compression algorithm in real time, and deploying system resources according to a monitoring result; and setting a multi-level cache, and caching different types of target data to the multi-level cache according to the divided types of the target data. According to the technical scheme, mass data can be efficiently stored and rapidly accessed, and the storage space utilization rate and the overall system performance are remarkably improved.

The invention discloses an interactive large-screen visualization system and method based on multi-dimensional data real-time rendering, relates to the technical field of image rendering, and aims to solve the bottleneck problems of a traditional system in the aspects of data throughput efficiency, rendering delay and interactive response. The method comprises the following steps: dividing space blocks by adopting a non-uniform octree, dynamically adjusting the size of the blocks according to data density, and combining a ZSTD + Hoffman hybrid compression algorithm; a CPU-GPU three-level asynchronous processing assembly line is constructed, and task priorities are dynamically adjusted through a fixation point weight algorithm; the master node dynamically allocates LOD parameters, and the slave nodes achieve special-shaped large-screen sub-pixel-level splicing based on a spherical coordinate system model; a Q-learning algorithm is adopted to predict a user operation sequence, and dual-channel bandwidth adaptive allocation is realized in combination with an ASTP protocol. The system comprises a dynamic partitioning module, a pipeline scheduling module, a multi-node cooperation module, an intelligent preloading module and an exception handling module. Real-time interaction visualization of a large amount of data can be realized, the rendering efficiency is improved, and hardware resource occupation is reduced.

The invention relates to the technical field of data compression and transmission, in particular to a dynamic data compression and transmission system and method based on big data. The method comprises the following steps: a data acquisition and processing unit acquires original data from various data sources and processes the original data; the data compression execution unit compresses original data through an LZ4 compression algorithm, and introduces a layered matching DEW-LZ4 technology based on dynamic entropy weight into the LZ4 compression algorithm to optimize the compression process; the data transmission unit performs network transmission on the compressed data through a transmission protocol HTTPS, and dynamically adjusts transmission parameters by adopting a machine learning Q-Learning model according to a network state; and a receiving end decompression unit obtains the transmitted compressed data from the network, decompresses the compressed data by using an LZ4 decompression algorithm, and recovers the compressed data to an original data format. According to the design of the invention, by introducing a layered matching DEW-LZ4 technology based on dynamic entropy weight, the system can intelligently select an optimal compression strategy according to different characteristics (Shannon entropy values) of data.

The invention discloses a distributed data storage management system and method, and relates to the technical field of distributed storage, and the method comprises the steps: obtaining an access log, metadata and content features of each node; data object attributes are judged, cold data are marked, and cold data distribution nodes are positioned; the method comprises the following steps: dividing cold data into permanent, periodic and sudden basic access modes through first-layer classification; generating a composite classification label through second-layer classification; matching a predefined strategy matrix according to a classification result, dynamically selecting a compression algorithm and a storage medium, and allocating bandwidth; operation auditing, compliance verification and exception handling are realized through the smart contract; and collecting strategy execution indexes in real time, and adjusting classification model parameters and resource scheduling weights. Through an intelligent cold data judgment and classification mechanism, cold data is accurately identified and managed, occupation of storage resources is reduced, and the overall utilization rate of the storage resources is improved.

The invention relates to the technical field of data processing, in particular to an artificial intelligence data aggregation method based on big data, and the method comprises the following steps: collecting traffic monitoring original data, removing repeated data and correcting wrong data through data cleaning and preprocessing, and generating preliminary cleaning data; dynamic index adjustment is applied to the preliminarily cleaned data, the depth and branches of an index tree are dynamically adjusted according to the access frequency and types of the data items, and optimized index data are obtained. In the dynamic index adjusting process, the index tree structure is dynamically optimized according to the data access frequency and type, the retrieval efficiency of high-frequency data is improved, and the storage overhead of low-frequency data is reduced. The hierarchical data compression performs hierarchical processing on the data, the use of storage space is greatly reduced through a mode of combining lightweight compression and an advanced compression algorithm, and a foundation is laid for large-scale data processing.

The invention discloses a high-performance AIOT real-time data processing method and device based on Flink, Kafka and Dores, and the method comprises the steps: constructing an end-to-end hierarchical architecture, capturing multi-source database change data in real time through Flink CDC, and analyzing semi-structured data in combination with Apache NiFi, and generating a standardized data stream; the method comprises the following steps: realizing message routing based on KafkaTopic division of a service domain, ensuring the consistency of a data structure by using Schema Registry, and optimizing the transmission efficiency through a dynamic compression algorithm; an Flink real-time computing engine is adopted to implement window aggregation, asynchronous dimension table association and incremental state persistence strategies, and the resource fluctuation and state management problems in streaming computing are solved; time pre-partitioning and intelligent cold and hot layering are achieved through a Dores storage layer, the query performance is improved in combination with materialized views and multi-level indexes, and finally a full-link closed loop of collection-transmission-calculation-storage-management is formed. According to the invention, the problems that the real-time performance and the consistency are difficult to balance and the system complexity is high in the prior art are solved.

The invention is suitable for the technical field of data asset management, and provides a data asset management system based on a block chain and big data analysis, and the system comprises a first terminal which carries out the storage of the ownership information of data assets through a block chain network, and generates a storage data package; generating an evaluation result based on a preset technical index analysis model; writing the hash value of the evaluation result into transaction data of the main chain of the block chain, and generating an evidence storage voucher matched with the block chain transaction; an interaction data sequence is packaged for identity signature, encryption and compression to generate a target code stream, and the target code stream is sent to the second terminal; the second terminal decrypts the encrypted data in the target code stream by using a private key, decompresses the data by using a compression algorithm matched with the first terminal, and restores the data into an interactive data sequence; verifying the access authority and the transaction condition of the evidence storage data packet; checking the anchoring state of the evidence storage voucher on the main chain of the block chain; and the data transaction is completed, the data asset state report is generated, and the data element marketization configuration efficiency is improved.

The invention provides a large language model distributed training method and system based on dynamic resource scheduling, and the method comprises the steps: collecting the resource state data of each calculation node at a periodic time interval, dividing a training task of a large language model into a plurality of types of subtasks in a current training batch, and carrying out the calculation of the subtasks; on the basis of the resource state data of each computing node and the task description data of each type of subtasks, allocating each type of subtasks to the optimal computing node at the optimal proportion by utilizing a reinforcement learning strategy; in addition, gradient data generated on the computational nodes are compressed by adopting a gradient compression algorithm, and the compression ratio of the gradient data is dynamically adjusted in combination with the current network bandwidth utilization rate of the computational nodes; and the parameter server performs weighted fusion on the gradient data compressed by different computing nodes based on the parameter synchronization interval, updates global model parameters based on a fusion result, and broadcasts the global model parameters to each computing node, so that the training efficiency of the large language model can be remarkably improved.

The invention discloses a digital twin-driven multi-source heterogeneous data fusion and intelligent analysis method for oil and gas pipeline construction. The method comprises the following steps: collecting multi-source heterogeneous data related to oil and gas pipeline construction; fusing data by using a space-time diagram neural network and constructing a dynamic offset compensation model to correct coordinate drift; constructing a data credibility dynamic evaluation chain, performing cross validation on sensor data to generate a confidence coefficient weight, and complementing missing data through a GAN complementing module to form a credibility enhanced data set; designing a layered distillation compression algorithm to compress the PINN model into a TinyLSTM engine, and developing a model increment updating protocol to realize efficient collaboration of a cloud end and an edge end; and training and optimizing a TinyLSTM engine by using the data set with enhanced credibility. According to the method, the defects in the aspects of data dynamic correction, credibility evaluation and complementation, model edge deployment real-time response and the like in the prior art are overcome, and the method has remarkable innovativeness and practicability.

The invention provides an attachment packing and unpacking method, system and device based on a compression algorithm and a medium, and belongs to the technical field of file compression. The method comprises the steps that all attachment files needing to be packaged are obtained by traversing a specified directory, screening and filtering are conducted according to file attributes, and a renamed file list and a path mapping table are generated; according to the renamed file list, using a Brotli algorithm to compress each attachment file based on a preset compression level, and packaging the compressed data into a packaged file containing metadata, compressed data and verification information; when the packed file needs to be unpacked, the legality of the packed file is confirmed through magic number verification and index analysis, file data are recovered through Brotli streaming decompression, and the original directory structure of the file is restored. According to the method, efficient compression and streaming decompression are achieved through the Brotli algorithm, and the attachment storage efficiency, the transmission speed and the data integrity are remarkably improved in combination with the intelligent screening and directory structure reduction technology.

The invention relates to the technical field of data acquisition, in particular to a bus real-time data acquisition method and system, and the method comprises the following steps: collecting data from a plurality of sensors of a bus in real time; dividing the data into a plurality of categories according to the timeliness and importance of the data; multi-level dynamic compression algorithm selection based on data categories: adopting multi-level dynamic compression algorithms for different categories of data; during data transmission, a self-adaptive priority scheduling mechanism is adopted, and the uploading priority of different types of data is dynamically adjusted according to the current network bandwidth and the transmission delay; in a bus network, short-distance wireless communication is established among a plurality of buses, and data cached in one bus is shared to other surrounding vehicles through a bus-bus linkage mechanism; according to the invention, real-time sharing and temporary supplement of data are realized through short-distance wireless communication, the problem of data missing caused by network disconnection of a single vehicle in a traditional data acquisition system is solved, and the reliability of overall data is greatly enhanced.

The invention relates to the technical field of data storage, and discloses a lossless compression algorithm-based data storage method, which comprises the following steps of: receiving to-be-stored data through a data interface, automatically identifying a data format by using a data analysis tool, and performing feature analysis to obtain the size, the type and the complexity of the data; through deep analysis of data features, the method can flexibly select a proper Huffman coding processing mode, and optimizes a compression process according to different data types and complexity. In addition, a context modeling optimization mechanism is introduced, the coding mode is dynamically adjusted, and the compression ratio and the storage efficiency are further improved; efficient storage and reliability verification of data are ensured through a block storage strategy in combination with metadata management; the problem that an existing compression algorithm is low in efficiency is solved, the integrity and reliability of data storage are improved, and the method is suitable for storage requirements of large-scale data and diversified data.

A CAE simulation result visualization system supporting multiple interaction modes comprises a multi-resolution rendering module used for obtaining interaction behaviors of a user and dynamically adjusting the resolution of a rendering area according to the interaction behaviors of the user; the key frame and time step screening module is used for analyzing the input simulation data, automatically screening out key frames and detecting the time step change of the simulation data according to a time sequence analysis method; the data compression and simplification module is used for compressing the data by using a feature-preserving compression algorithm, performing incremental storage and sparse representation on the compressed data, and recording data changes; and the dynamic local playing and frame-skipping rendering module is used for acquiring a user local time playing instruction, and processing user local time and non-user local time respectively through a frame-skipping rendering method so as to realize multi-frame rendering and switching. According to the method, the data analysis efficiency is improved, the rendering and data transmission efficiency is improved, and computing resources are saved.

The invention relates to the technical field of data processing, in particular to a load awareness and data dynamic layout method and system based on deep learning, and the method comprises the steps: collecting hardware load indexes of all nodes in a distributed cluster in real time, and carrying out the preprocessing of the hardware load indexes; based on the preprocessed hardware load index, calculating a comprehensive load score of each node through a weighted fuzzy logic algorithm; dividing load intervals according to the comprehensive load score and matching a predefined strategy combination; and dynamically adjusting a compression algorithm, a compression ratio and a fragment merging parameter based on the current service flow peak-valley state of the target data block and a matched predefined strategy combination, the service flow peak-valley state being determined through a request processing rate and response time. The objective of the invention is to realize dynamic and fine-grained load sensing and data layout.

The invention relates to the technical field of data processing and communication, in particular to a multi-source heterogeneous data compression and transmission method and system for an intelligent fusion terminal, and the method comprises the steps: collecting multi-source heterogeneous data from different data sources through the intelligent fusion terminal, carrying out the data preprocessing of the collected multi-source heterogeneous data, and obtaining a data preprocessing result; comprising format unification and noise filtering; establishing a data feature analysis model, and performing feature extraction and classification on the preprocessed data; according to data feature extraction and classification results, dynamic weights are allocated to different types of data, and the data are divided into data blocks of different sizes according to the allocated dynamic weights and data features; for different types of data blocks, the priority of the data blocks is set, a proper compression algorithm is selected for compression, and the compressed data is transmitted through a network. The multi-source heterogeneous data is subjected to feature analysis and targeted compression processing, so that network bandwidth occupation and equipment energy consumption are reduced, and data transmission efficiency is improved.

The invention relates to the technical field of data transmission of the Internet of Things, in particular to a self-adaptive data compression and transmission method of a low-power-consumption wide-area Internet of Things. According to the method, a three-layer collaborative architecture comprising an equipment layer, a fog computing layer and a cloud computing layer is constructed, data preprocessing and feature analysis are performed on the equipment layer, and data types, entropy values and repeatability information are extracted; the fog calculation layer selects an optimal compression algorithm based on a multi-dimensional decision engine, and reduces redundancy through spatial-temporal correlation analysis and data aggregation; the cloud computing layer collects compression performance data, adopts reinforcement learning and federal learning to train a global optimization model, and dynamically issues strategy parameters; and the fog node adaptively adjusts a compression strategy in combination with the system state to realize the optimal balance between the compression ratio and the reconstruction precision. The method is suitable for field deployment environments with limited electric quantity and network, has the advantages of low power consumption, high efficiency, strong adaptability and the like, and can be widely applied to Internet of Things scenes such as remote monitoring, smart energy and the like.

The invention relates to a three-dimensional model compression transmission method and system based on dynamic feature perception, and relates to the technical field of three-dimensional live-action modeling, and the method comprises the steps: obtaining the original three-dimensional data of a three-dimensional model, carrying out the multi-modal feature extraction based on a model scene, dividing a key region, a transition region and a non-key region, and generating a three-dimensional model partition map, compressing each region according to the partition map label and a preset compression algorithm to generate a multi-resolution LOD sequence, determining a transmission data hierarchy in combination with a network state and equipment parameters, and finally rendering the model according to the transmission data hierarchy, user behavior information and an environment state to obtain a target three-dimensional model. The technical effects of effectively compressing, transmitting and rendering the three-dimensional model according to various factors such as different area characteristics, network states, equipment parameters and user behaviors of the model and improving the transmission efficiency and the rendering quality are achieved.

The invention relates to a data management method and a data management system based on OpenHarmony. The method comprises the following steps: constructing a cross-device data storage layer by utilizing a distributed soft bus technology, and carrying out fragmentation storage and redundant backup on target data to obtain a distributed data storage architecture; performing feature extraction to obtain a data classification result and a fine-grained data mark; comprehensively analyzing the network condition, the equipment performance and the user behavior to obtain a data synchronization strategy; executing two-stage protocol submission and vector clock concurrency control on the cross-device operation to obtain a distributed transaction processing result; performing structural feature and repetitive mode analysis to obtain a compression algorithm selection result and a compression parameter; and carrying out compression processing, and carrying out adaptive routing selection and load balancing on the compressed data transmission path to obtain a multi-device data flow management scheme. According to the method, data integrity and availability are guaranteed, data are effectively compressed, and storage space and transmission bandwidth are saved.

The invention discloses an efficient data compression and transmission method based on a computing power host management operation platform, and relates to a large-scale data concurrent management technology. Comprising the steps of 1, integrating a lightweight data feature analyzer at a Kafka producer end, extracting feature indexes of data streams in real time through the data feature analyzer, and generating compression strategy parameters according to the feature indexes, and 2, performing feature recognition on structured data and unstructured data, extracting data features, and generating compression strategy parameters according to the compression strategy parameters. And step 3, based on data characteristics, CPU load of producers / consumers and network bandwidth prediction, constructing a multi-objective optimization model, dynamically selecting a compression algorithm and a compression level, selecting compression strategy parameters according to the compression algorithm, introducing a compression income ratio index, calculating a ratio of compression rate increase to compression time consumption, and performing compression strategy optimization. And preferentially selecting a compression algorithm with a high gain ratio to perform data compression and transmission.

The invention relates to the field of hardware encryption, and discloses a geological disaster monitoring scene-based low-power-consumption hardware encryption system, which comprises a layered architecture module for constructing a terminal sensing layer and a cloud platform layer, generating a three-dimensional risk thermodynamic diagram, and carrying out short-term early warning model and emergency deduction; the encryption and decryption module is used for carrying out multi-dimensional identity verification based on protocol layering and national secret algorithm fusion, carrying out dynamic management and eavesdropping monitoring on a secret key, optimizing a data processing flow, and establishing a secure channel to dynamically negotiate whether encryption or decryption is needed; the key management module is used for implementing a triple binding mechanism, carrying out processing and risk assessment on the monitoring data, and carrying out key backup and recovery by adopting a dynamic key period according to a generated risk level; and the safety protection module is used for performing intelligent management on the power consumption of the system based on the active state of the system, selecting a compression strategy according to the data type by adopting a dynamic compression algorithm, performing safety monitoring on hardware and firmware, performing intrusion monitoring on the system and performing automatic repair.

The invention discloses a time series data lossless compression method and system based on dynamic context awareness, and relates to the field of data lossless compression, and the method comprises the steps: extracting target data from a device port, and carrying out the preprocessing of the target data through a data processing technology according to the business dimension corresponding to the target data, constructing a structured data set based on a preprocessing result; capturing local time sequence features based on the structured data set, distributing attention weights in combination with a hidden state, generating a feature vector used for sensing a context, and outputting a dimension vector by using a three-layer full-connection network; and according to the dimension vector, analyzing the compression performance score value of each compression algorithm in the resource library, sorting the compression performance score values, and selecting the compression algorithm meeting the target requirement to compress the target data. According to the method, the global periodic features and the local mutation features of the time series data are analyzed through the space-time attention mechanism, and real-time driving algorithm switching is achieved.

The invention provides an image data processing and storing method based on a meta universe game, which comprises the following steps of: integrating and primarily processing image data to obtain an original image data set; a graphic database is designed based on a distributed storage system and by adopting a cold and hot data hierarchical storage strategy; processing the original image data set by utilizing a self-adaptive compression algorithm, a multi-thread asynchronous loading mode, GPU (Graphics Processing Unit) parallel computing, a progressive rendering technology, a physical basic rendering technology and illumination computing to obtain a standard image data set; a virtualization technology is utilized to simulate various hardware configurations, and a plurality of APIs are combined to carry out cross-platform compatible management of image data; and automatically updating and optimizing the content of the image data according to the monitoring result of the image data. According to the method, efficient storage and processing of the image data can be realized, the quality and compatibility of the image data are enhanced, the game performance and the user experience are remarkably improved, and development and popularization of the virtual world are promoted.

The invention provides a time sequence data compression-based database monitoring operation and maintenance method and system, which realize real-time monitoring of various index data of a database platform through an efficient data acquisition and compression technology. The system can efficiently collect various performance data generated in the operation process of the database, optimizes time series data through a compression algorithm, and reduces redundant storage of the data. In a data storage and management process, a primary differential sequence and a secondary differential sequence are calculated, so that the stored data volume is reduced; according to the method, a trend in an original data sequence is removed by using difference, so that a subsequent difference sequence is easier to compress, and meanwhile, for non-constant difference, by setting a dynamic threshold value and only recording a difference value greater than the threshold value, small changes are ignored and the compression ratio is improved on the premise of ensuring that important data information is not lost. And on the other hand, the regularity of the differential sequence is judged through Srelative, and a compression strategy can be selectively applied, so that proper compression methods can be specifically applied to time sequence data with different characteristics, and the processing efficiency is improved. Through the design, the storage and processing efficiency of the compressed data is improved, the system can decompress and analyze the data more quickly, and the running state of the database is monitored in real time.

The invention discloses an efficient intelligent data compression and feature extraction method and system. The method comprises the following steps: S1, data preprocessing: performing data cleaning, denoising and standardization processing on to-be-processed data; s2, data compression and storage: determining a compression algorithm according to the type of the data, and uploading a compressed data file to a database; s3, feature extraction: deploying a machine learning model, and extracting representative features from the compressed data; and S4, data post-processing: further processing and optimizing the extracted features. According to the method, the data compression technology and the feature extraction technology are combined, the most representative information is extracted from the original data, meanwhile, the data storage and transmission cost is reduced, key features in the data are automatically recognized and reserved through an intelligent algorithm, and therefore efficient data compression and feature extraction are achieved.

The invention belongs to the field of natural gas pipeline operation and maintenance, and particularly discloses an intelligent monitor for multi-parameter fusion analysis in natural gas pipeline operation. The sensor module is arranged on a natural gas pipeline and is used for acquiring original data and signal intensity; the preprocessing module preprocesses the original data to obtain preprocessed data, and then adopts a self-adaptive compression algorithm to obtain compressed data; the fusion module adopts a multi-source data fusion algorithm for the compressed data to obtain fusion features; the diagnosis module inputs the fusion features into a fault diagnosis model based on a machine learning algorithm, and when it is diagnosed that a fault exists, the fault type is output; the positioning module obtains the position, geographic information data and historical operation data of the sensor module, and obtains a fault position by adopting a positioning algorithm according to the signal intensity, the position, geographic information data and historical operation data of the sensor module and a fault type; and the early warning module generates a fault report according to the fault position and the fault type, sends out an early warning signal and uploads the fault report.