1040 results about "Data redundancy" patented technology

The invention discloses a multi-dimensional carbon flux monitoring method, and particularly relates to the technical field of carbon flux monitoring, and the method comprises the following steps: collecting multi-source heterogeneous sensor data, and carrying out spatial resolution unification, time synchronization alignment and data format standardization preprocessing to ensure data consistency; executing fusion validity detection, and correcting time synchronization deviation and data redundancy conflicts; according to an abnormal detection result, dynamically adjusting a fusion weight or determining a weight based on historical clustering, and combining spatial adjacency interpolation and time interpolation to realize data fusion; and finally, inputting a carbon flux inversion model, and outputting a high-precision carbon flux monitoring value to realize dynamic monitoring and prediction. According to the method, a dynamic fusion weight adjustment mechanism is adopted, the fusion weight of each data source is dynamically optimized, adaptive fusion of multi-source observation data is realized, the problems of data discontinuity and insufficient data coverage in the prior art are solved, and a multi-dimensional, full-coverage and high-resolution carbon flux fusion data set is ensured to be formed.

The invention discloses a power equipment intelligent gateway fault detection system based on the Internet of Things, relates to the technical field of power equipment intelligent gateways, and is used for solving the problem that an existing power equipment monitoring system is inaccurate in fault detection. Comprising a power equipment access module, a data preprocessing and edge calculation module, a data storage and analysis module and a fault diagnosis and alarm module which are in signal connection. The power equipment access module is used for collecting operation state data of power equipment; the data preprocessing and edge calculation module performs cleaning, format conversion and preliminary calculation on the collected data, so that data redundancy is reduced, and the processing efficiency is improved; the data storage and analysis module stores the processed data and extracts feature information by using a big data analysis method; and the fault diagnosis and alarm module carries out fault identification based on an analysis result and triggers an alarm signal so as to realize accurate detection and efficient maintenance of the intelligent gateway of the power equipment.

The invention discloses a multi-parameter in-situ detection data analysis system for a marine ranch, and relates to the technical field of data analysis, the multi-parameter in-situ detection data analysis system comprises a multi-parameter sensor group and a self-adaptive multi-dimensional trigger type sampling control module, the multi-parameter sensor group comprises an environmental parameter sensor and a biological behavior sensor, the environmental parameter sensor is used for measuring real-time environmental parameters of the marine ranch, the biological behavior sensor is used for monitoring biological behavior parameters of the marine ranch in real time, and the self-adaptive multi-dimensional trigger type sampling control module comprises a data preprocessing unit and a dynamic sampling strategy unit. The self-adaptive multi-dimensional trigger type sampling control module is designed, so that sampling modes are dynamically switched according to real-time dual-mode data, environment sudden changes are effectively captured, data redundancy or key change sampling leakage caused by traditional fixed sampling frequency is avoided, monitoring precision is improved, energy optimization is achieved, the service life of equipment is prolonged, and the system is suitable for large-scale popularization and application. And high quality and real-time performance of data are ensured.

The invention relates to the technical field of anomaly detection, in particular to a measurement abnormal data detection method and system based on a rule engine, and the method comprises the following steps: extracting a transmission path and a timestamp sequence based on a source point number, a transmission link number and a real-time state code of a measurement data packet, comparing a data flow direction path with a preset abnormal flow direction, and determining a data flow direction; and identifying an abnormal mode of which the risk path fitting degree exceeds a preset threshold value, and obtaining an abnormal flow direction feature set. According to the method, abnormal point locations in the data can be effectively identified and corrected by monitoring the change of the submission times and the activeness of the real-time measurement data of the power grid and the data synchronism, and the dynamic abnormal threshold setting and real-time data analysis further enhance the capability of capturing the abnormal trend, can adjust the processing strategy in time, and improve the accuracy of data processing. The method has the advantages that accumulation and propagation of error data are avoided, data redundancy and error repetition are effectively reduced due to generation of a data calibration set and application of data fingerprints, and data quality is guaranteed.

The invention provides a road slope stability prediction method and device, a storage medium and electronic equipment, and the method comprises the steps: collecting the multi-dimensional environmental parameters of a road slope in real time, and forming an original parameter set; based on a Granger causal test algorithm, analyzing the original parameter set, determining a causal influence relationship among the parameters, constructing a parameter causal network, and screening out key parameters; calculating the similarity of each key parameter in time and space dimensions through a dynamic time warping algorithm, and generating a space-time incidence matrix; based on the space-time incidence matrix, performing weighted fusion on the key parameters to obtain fusion parameters; inputting the fusion parameters into a pre-trained slope stability prediction model to obtain a predicted value of the slope stability; and when the predicted value exceeds a dynamically updated early warning threshold value, outputting slope instability early warning information. According to the method, the defects of inaccurate prediction and data redundancy of a current road slope stability prediction method are overcome.

The invention relates to the technical field of video monitoring, and discloses a camera cooperative monitoring method and system based on multi-modal perception, and the method comprises the steps: detecting an abnormal event signal in an environment through a non-visual sensor node, triggering the activation of a camera, and carrying out the visual capture of a target region; the camera performs relay tracking based on target feature matching to generate a continuous motion track; performing multi-view collaborative shielding on privacy sensitive information in the tracking target, generating desensitized monitoring data, and uploading the desensitized monitoring data; and optimizing the energy consumption of the camera in a non-event triggering period, and updating the behavior recognition model based on the desensitization data. According to the method, the contradiction between privacy protection and monitoring efficiency is solved, cross-regional model evolution and energy consumption reduction are realized, and the problems of response lag, data redundancy, privacy disclosure and high energy consumption of traditional monitoring are systematically avoided.

The invention relates to the field of channel management, in particular to an intelligent channel management method and system based on multi-source data fusion. The invention discloses an intelligent navigation channel management system based on multi-source data fusion. The system comprises a combined ship identification module, a positioning frequency updating module, a collision risk assessment module and an intervention management module. According to the invention, the system can sense multi-dimensional environmental parameters such as illumination intensity, visibility, AIS signal quality, radar echo signal-to-noise ratio and the like in real time by constructing a combined ship identification model containing AIS, radar, vision and infrared, and dynamically adjust the fusion weight of each sensor to compensate for the deficiency of a single sensor; according to the method, the robustness of ship dynamic monitoring is improved, high-precision tracking can be kept in severe weather, the data redundancy contradiction of a traditional multi-sensor system is avoided, and therefore continuous and stable target state input is provided for follow-up risk analysis.

The invention belongs to the technical field of data processing, and discloses an information technology analysis system and method based on cloud computing, and the method comprises the steps that a network building module caches static data to nodes nearby a user through a CDN; the resource scheduling module monitors and dynamically allocates resources in real time; the data storage module realizes data redundancy storage; the data acquisition module performs multi-source data acquisition; the data integration module integrates data of different data sources; the data management module manages the whole data process; the data analysis module carries out multi-source data fusion analysis and mines a potential relationship; the result display module provides an intuitive and easy-to-use interface; the security management module guarantees data storage and transmission security; and the monitoring management module deploys a monitoring tool to monitor system operation in real time. According to the method, dynamic allocation and efficient utilization of resources are achieved, deep data analysis is achieved, training is accelerated through distributed computing, automatic model updating is achieved, multi-source data fusion and deep mining are conducted, and a scientific basis is provided for decision making.

Systems and methods facilitate saving data storage through data storage management of storage location(s) by training, via an iterative training and testing loop, a predictive model using training data to detect data redundancies from two or more datasets stored to data storage location(s), the training includes testing the predictive model by predicting a target variable and iteratively adjusting weights and calculations during each subsequent iteration to improve predictability of the target variable, where the predictive model is trained to identify data similarities among the two or more datasets. Based on any error in predicting the target variable being less than a predetermined level, the predictive model is deployed and applied to at least two datasets to quantify a percentage of similarity among the at least two datasets. If it is determined the percentage of similarity surpasses a predefined threshold percentage, then an electronic notification is transmitted.

The invention provides a cache data cleaning method and device, electronic equipment and a storage medium, and relates to the technical field of computer data storage.The method comprises the steps that a transition controller is used for replacing a fault controller for mirror image recombination, and a temporary mirror image pair is formed; it can be ensured that the basic data redundancy and the read-write function of the storage system are not seriously influenced; when the fault controller is recovered, the complex fault condition can be sensed in real time; once the concurrent fault occurs, the data cleaning process is adjusted according to execution state information of the data cleaning process, team information of the temporary mirror image pair and concurrent fault information of a concurrent fault controller; the data of the transition controller can be effectively cleaned under the condition of concurrent faults, and the fault-tolerant capability of the storage system is improved; when the cache data of the transition controller is cleaned, the overall performance and efficiency of the storage system are improved through an effective fault processing mechanism and accurate data cleaning process adjustment.

The invention discloses an edge-cloud collaborative civil aviation equipment intelligent detection platform, which relates to the technical field of intelligent detection and comprises an edge computing layer, a cloud computing layer, a collaborative scheduling layer, a data collaborative channel and an image processing layer. According to the method, a dynamic edge-cloud task scheduling mechanism is adopted, a traditional static task allocation mode is broken through, millisecond-level elastic task migration is achieved through real-time network state monitoring and computing power perception, incremental federated learning collaborative modeling and a gradient aggregation mechanism based on differential privacy are adopted, collaborative optimization of a multi-edge-node model is supported, and the task migration efficiency is improved. Multi-modal sensor fusion and lightweight anomaly detection are fused, the composite fault detection precision is improved, adaptive sampling and an edge knowledge graph are adopted, the sampling rate is dynamically adjusted based on the equipment risk level, offline rapid diagnosis can be achieved in combination with a graph database, and the problems that data redundancy is caused by fixed sampling, and the detection accuracy is high are solved. And the traditional system loses the diagnosis capability when the network is interrupted.

The invention relates to the technical field of three-dimensional model reconstruction, in particular to an existing building three-dimensional model reconstruction system based on point cloud data, and the system comprises a point cloud data processing module which carries out the octree recursive space subdivision of input point cloud data, builds a hierarchical index structure, and generates a multi-view fusion point set. In the method, octree recursive space subdivision is performed on the input point cloud data to establish a hierarchical index structure, so that the key area of the building surface can be positioned more accurately in subsequent data processing, data redundancy is reduced, and the precision and efficiency of three-dimensional model construction are improved; then voxel depth field data is obtained by calculating a truncation symbol distance function value of voxels, the description accuracy of a building surface structure is improved, and an initial surface grid is formed through contour surface extraction; meanwhile, plane slicing is conducted on the multi-view fusion point set in the preset building main axis direction, and contour details of all building structure sections are captured.

The invention relates to the technical field of indoor environment monitoring, and discloses an indoor temperature and humidity acquisition system based on a distributed architecture, which comprises a distributed sensor node cluster, a data aggregation gateway and a central processing unit. The distributed sensor node cluster is deployed in different indoor areas to collect data, the data aggregation gateway preprocesses the data, and the central processing unit divides the areas according to spatial and temporal distribution characteristics of the data, constructs various data collection strategies, calculates node energy consumption estimation, data redundancy and transmission delay, and screens an optimal strategy to control node sampling and communication. The system is further provided with a fault-tolerant module, an edge calculation unit and the like, and can detect faults, predict trends, adjust strategy weights and the like. The system is comprehensive and accurate in data acquisition, good in data processing and transmission performance, intelligent in node management and high in environmental adaptability, has expansibility and collaboration, and can be widely applied to temperature and humidity monitoring of various indoor places.

The invention discloses a rapid construction method for a standard knowledge graph of railway bridge design. The method comprises the steps that S1, data are collected and preprocessed; s2, a BERT pre-training model and a Bi-LSTM model are adopted to convert a text sequence in the processed data into a labeled tag sequence {y1, y2... yn}; s3, identifying and labeling entities, attributes and relationships thereof by using a conditional random field (CRF) and a graph neural network (GNN) technology, forming nodes and edges of the knowledge graph, and obtaining a complete knowledge graph (KG); and S4, adjusting the complete knowledge graph KG structure in real time by using a dynamic topological optimization algorithm. According to the method, the standard data is rapidly integrated and optimally designed, data redundancy is reduced, and newly added and changed data are effectively processed, so that the construction speed and the maintenance efficiency of the knowledge graph are improved.

The invention discloses a reinforcement learning driving-based cavern group distributed optical fiber temperature field dynamic monitoring system, and relates to the technical field of underground engineering safety monitoring, and the system comprises a distributed optical fiber sensor array which is crosswise and annularly arranged along the inner wall of a cavern group and is used for collecting original optical signals containing temperature and strain coupling. According to the cavern group distributed optical fiber temperature field dynamic monitoring system based on reinforcement learning driving, through a dynamic coupling and decoupling mechanism of reinforcement learning and a physical model, the precision bottleneck of a traditional optical fiber temperature monitoring technology under complex multi-physics field interference is broken through. A dual-channel sensing network and a self-evolution interference feature library are constructed, real-time adaptive separation of strain-temperature components is realized, a dual heterogeneous verification mechanism is introduced, and the credibility of a decoupling result is guaranteed from two dimensions of a physical rule and data redundancy; and meanwhile, through closed-loop optimization and redundant fault-tolerant design, the monitoring robustness and continuity in a complex engineering environment are remarkably improved.

The invention discloses a flexible surgical instrument-oriented multi-source sensing data adaptive fusion method and a related device. The method comprises the following steps: acquiring optical, mechanical and pose multi-mode sensing data based on a timestamp synchronization technology in a working environment; clock compensation and space coordinate transformation based on an instrument kinematics model are carried out on the multi-modal data, and space-time registration is completed; inputting the registered data into a neural radiation field model to generate continuous space implicit scene representation; inputting the implicit representation into a pre-trained RWKV fusion network to obtain a fusion feature; and superposing the fusion feature and the original low-frequency component through residual connection, and integrating to generate three-dimensional voxelization environment state data. According to the method, multi-source information high-fidelity fusion is achieved through a single path, data redundancy is remarkably reduced, and the real-time performance and precision of intra-operative environment perception are improved.

The invention relates to the field of data processing, in particular to a data acquisition method and system based on a multi-source water affair sensor, and the method comprises the steps: calculating period relevance and trend relevance, and respectively obtaining abnormal probability predicted values based on a period rule and a trend rule, taking the period relevance and the trend relevance as weights, and carrying out weighted summation on the two abnormal probability predicted values to obtain a comprehensive abnormal probability predicted value; calculating the abnormal relevance of each kind of historical influence factor data, and obtaining the abnormal possibility of the water affair data according to the abnormal relevance; correcting the comprehensive abnormal probability predicted value according to the abnormal possibility of the water affair data to obtain a final abnormal probability predicted value; setting an acquisition frequency according to the final abnormal probability predicted value; and performing water affair data acquisition control based on the acquisition frequency. And the water affair data acquisition frequency is adaptively set, so that abnormal information can be grasped in time while data redundancy is reduced.

The invention relates to a business process standard dynamic optimization method and system based on a large language model, and belongs to the technical field of computer application, and the method comprises the following steps: constructing a data collection strategy template; performing anomaly detection on the key field based on a preset three-level anomaly detection mechanism to obtain standard structured data; constructing a process execution accurate quantitative evaluation model, and constructing a process topological graph by using a process mining technology; calculating an abnormal probability of each node in the process topological graph by using a multi-task graph neural network, and obtaining a high-attention area in the process topological graph based on the abnormal probability; analyzing the multi-source heterogeneous data associated with the high-attention area through a large language model, positioning root causes, calculating root cause influence weights, and obtaining a root cause list; establishing an intelligent bidirectional feedback mechanism to generate optimization suggestions of the standard process; according to the method, the problems of serious data redundancy, insufficient standard dynamics and lack of feedback mechanisms in the traditional flow standard optimization technology in the power industry are effectively solved.

The invention relates to the technical field of data recording, in particular to a data recording system for an automatic driving vehicle, and the system comprises a multi-source signal synchronization module, a behavior stage recognition module, a track abnormity elimination module, a dynamic compression adjustment module and a segmented index marking module. According to the method, a unified time alignment mechanism is constructed in a signal synchronization processing link, a strict corresponding relation between acquisition sequences is ensured, key time sequence characteristics of driving behavior switching are extracted by adopting a multi-behavior parameter comprehensive judgment method, segmented expression of a complex driving process is realized, and the accuracy of driving behavior switching is improved. According to track anomaly identification, through collaborative screening of inertial data and a positioning track, abnormal fragments which do not conform to physical rules are effectively eliminated, data compression dynamically sets interval length according to a parameter combined fluctuation trend, the fidelity of a key period is remarkably improved, non-key data redundancy is inhibited, and after a segmented index structure is established, the data compression efficiency is improved. And the data retrieval process is clearer.

The invention provides a wireless sensing platform with an edge computing function, and relates to the technical field of wireless sensors. In order to solve the problems that in the prior art, a wireless sensor is large in data redundancy, high in transmission delay and weak in emergency recognition and response capacity in a complex scene, a joint processing mechanism combining a causal event recognition assembly, a causal data fusion assembly, an event separation assembly and a data distribution assembly is adopted. By detecting a causal change event in an environment and dynamically extracting and fusing sensor data with a strong causal relationship, redundant data transmission caused by emergencies is reduced, transmission delay is reduced, the robustness of causal data fusion in a concurrent event scene is improved, and the reliability of data fusion is improved. According to the invention, efficient and accurate sensing and response to emergencies in a complex environment are realized.

The invention discloses a vector coprocessor and a vector calculation method, and relates to the technical field of processors. The vector coprocessor comprises an arbitration module, a distributor module, a function module and a data path; the arbitration module is used for receiving the calculation tasks from the main processor, screening vector tasks from the calculation tasks and filtering non-vector tasks in the calculation tasks; the dispatcher module is used for determining the parallelism degree and the task queue of the vector tasks and dispatching the vector tasks according to the parallelism degree and the task queue; the function module is used for decomposing the vector task into a plurality of subtasks and determining a data path corresponding to each subtask; distributing the subtasks to the data paths, and reducing the calculation results corresponding to the data paths to generate target results corresponding to the vector tasks; and the data path is used for calculating the subtasks and sending a calculation result to the functional module. Data redundancy storage is avoided, and the working efficiency of the processor in vector calculation is improved.

The invention discloses a power distribution network fault knowledge graph construction method and system based on multi-modal data, equipment and a medium, and belongs to the technical field of intelligent power grids, and the method comprises the steps: carrying out the change matching of spatial position signals and meteorological parameters based on regional monitoring space-time records, meteorological environment parameters and equipment operation state data, dividing time periods, and obtaining a power distribution network fault knowledge graph; and analyzing according to spatial position association, and screening data overlapping to obtain a multi-modal data association distribution table. According to the invention, through matching and screening of space-time records, meteorological parameters and equipment state data, multi-modal data integration and abnormal feature extraction are realized, and in combination with cross analysis of signal fluctuation and meteorological conditions, abnormal signal classification and path association are optimized, and a fault propagation rule is accurately analyzed. The overall optimization significantly improves the efficiency of abnormal recognition, fault location and propagation analysis of the power distribution network, enhances the operation reliability and fault processing capability, reduces data redundancy and errors, and improves the safety and stability of the power grid.

The invention discloses a tangelo ecological orchard data management platform, relates to the technical field of agricultural informatization and intelligent management, is used for solving the problem that decision-making efficiency is affected by data delay, and realizes real-time acquisition and classified storage of orchard environment data by arranging a sensor node network and a distributed storage system. Through data preprocessing and feature extraction, the data redundancy is reduced, and the analysis efficiency is improved; an orchard environment change trend is predicted by using a time sequence analysis algorithm, and a scientific basis is provided for resource allocation; the generation of a dynamic resource allocation scheme is realized by introducing an adaptive weight allocation mechanism and a resource priority index; and an interaction module is deployed at the orchard management terminal, so that a user can check data and adjust resource allocation in real time, and the intelligent level of orchard management is improved.

The invention relates to the technical field of catch safety tracing, and discloses a block chain verification method for catch quality safety tracing. The method comprises the following steps: acquiring environmental parameter data of catches in the links of fishing, transportation and storage through a distributed sensor network, and encrypting according to a timestamp sequence to generate a catches environmental data chain; key environment indexes are extracted based on the data chain, an abnormal deviation degree is calculated by using a dynamic weight distribution algorithm, and a catch quality risk score is generated; dividing risk levels according to the risk scores, mapping data of different risk levels to a block chain differentiation storage layer, and constructing a fragment storage structure; performing redundancy verification on the high-risk data through an intelligent contract triggering mechanism, and calling a consensus node to perform cross verification to generate a verification result; and performing association analysis on the verification result and the risk score, updating the risk tag and synchronizing the risk tag to a block chain distributed account book. According to the method, full-link data tracing and verification of the catches are realized, and the data credibility and the quality control accuracy are improved.

The invention relates to the technical field of data transmission optimization, in particular to a low-power-consumption data transmission optimization system and method for a smart watch. According to the method, the data transmission strategy is dynamically adjusted by combining the user behavior state, the data content characteristics and the system operation parameters. The system firstly senses the wearing state, interaction frequency and equipment resource information of a user, evaluates the priority of to-be-transmitted data, predicts the short-time attention probability of the user based on the behavior trend, and calculates the cognitive perception index of the data. And further comprehensively considering energy consumption, cognitive risk and resource occupation through a scheduling cost function, selecting an optimal transmission mode, and executing instant uploading, delayed caching, compression processing or communication protocol switching operation at an equipment end. According to the scheme, the data redundancy transmission frequency can be effectively reduced, the timeliness of key data is ensured, the overall energy efficiency of the system is remarkably improved, and the method is suitable for the smart watches in various dynamic use scenes.

The invention discloses a child growth and development monitoring and evaluation method based on medical big data, and relates to the technical field of medical health and child growth and development monitoring. The method comprises the following steps: step 1, respectively reading height data, weight data, corresponding acquisition time points and diagnosis records of children from databases of at least two medical institutions; according to the child growth and development monitoring and evaluation method based on the medical big data, the fine piecewise linear reference growth interval is established, the difference coefficient is quantified, and multiple threshold detection is performed, so that data redundancy and misjudgment are effectively reduced, and early recognition and timely intervention of potential abnormalities are realized by using a dynamic monitoring interval and a hierarchical intervention mechanism. Through regular review and adaptive adjustment, and sustainable optimization of a reference model and a threshold value, the defects of incomplete data, dull monitoring frequency and difficulty in timely capture of abnormity in a traditional method are overcome, and child growth and development monitoring has accuracy, flexibility and expandability.

The invention discloses a fire equipment management method and system based on a railway tunnel fire-fighting monitoring system, and relates to the technical field of tunnel fire fighting, and the system comprises a fire-fighting monitoring module which is used for collecting multi-dimensional state data of fire equipment in real time; the redundant data processing module adopts a hot standby redundancy mechanism and is used for analyzing and processing the multi-dimensional state data of the fire equipment in real time and generating a fire risk assessment report; an emergency scheduling plan is called based on the fire risk assessment report, and an execution instruction is generated and issued to the execution module; the execution module is used for controlling fire equipment to work according to the execution instruction; and the data display module is used for displaying the multi-dimensional state data, the fire risk assessment report and the emergency scheduling plan of the fire equipment in real time. According to the invention, potential safety hazards can be found earlier, early warning of fire risks is realized, the accuracy and timeliness of fire warning can be obviously improved, and thus precious time is won for taking effective prevention measures.

The invention discloses a distributed data encryption transmission method and system, a storage medium and a program product, and the method comprises the steps: carrying out the layering and fragmentation processing of to-be-transmitted data, and obtaining a plurality of main fragments and a plurality of main fragment index tables; dividing each main fragment into a preset number of sub-fragments and generating a sub-fragment mapping table; generating verification data for each sub-fragment according to the data redundancy; the verification data and the corresponding sub-fragments are combined to generate redundant fragments; constructing a transmission control tree for each main fragment based on a network link state; generating node affinity according to the transmission control tree, and converting the node affinity into a time sequence key seed; performing encryption processing on the redundant fragments by using the time sequence key seed to generate a verification feature sequence; combining the redundant fragments with the verification feature sequence to form a transmission data packet; and transmitting the transmission data packet according to the node hierarchical relationship of the transmission control tree. According to the invention, the integrity and consistency of data fragmentation are improved, and the reliability and security of data transmission are further improved.

The invention relates to the technical field of chip design, and provides a critical path screening method based on a chip time sequence report, which comprises the following steps: collecting chip time sequence report data, screening out first N critical paths from the data based on time sequence index data, and establishing a path ID-delay type-scene three-dimensional data model; and calculating the delay ratio of the unit delay to the interconnection line delay in each critical path. Performing single-type delay, type relevance and RC condition influence comparison on different scenes to obtain delay characteristic data. The invention discloses a system for executing the method, which can avoid full data redundancy and improve the analysis efficiency by screening the first N critical paths. And the delay change rule in different scenes is quantified. The delay characteristic data reflects the actual influence of the key path delay on the time sequence, so that a designer can quickly position the delay bottleneck of the key path, and a basis and a reference are provided for the targeted optimization of the total delay of the chip in the later period.

The invention discloses a multi-edge-node collaborative industrial energy consumption data compression and storage method and system, particularly relates to the technical field of data compression and storage, and realizes hierarchical compression processing by layering industrial energy consumption data acquisition equipment into a main node, a core slave node and a terminal slave node. According to the method, various compression algorithms are used for carrying out lossy or lossless compression on different types of data, and cooperative compression and data redundancy elimination between edge nodes are realized under the coordination of a main node by introducing a distance calculation mechanism of'node hierarchy + auxiliary parameters'. The system constructs a node correlation graph through data abstracts, optimizes a data transmission path based on reinforcement learning, and adopts a cold and hot data classification strategy to improve the storage efficiency. The data compression efficiency and reliability are improved, and the method is suitable for real-time processing and long-term storage of large-scale industrial energy consumption data.