1334 results about "Data redundancy" patented technology

The invention discloses a large-model-assisted self-lifting multi-modal industrial equipment knowledge graph construction method. The method comprises the following steps: collecting and preprocessing multi-modal data; obtaining structured knowledge data in a triple form based on a few-sample prompt; gradually self-optimizing the triple from three evaluation dimensions in combination with thinking chain skills to ensure normalization, integrity and reliability of knowledge expression; the problems of data redundancy and ambiguity in the knowledge graph are solved by adopting a triple-step entity alignment method and utilizing the self-lifting capability of a large model; based on a knowledge graph completion template and a retrieval enhancement mechanism, a relationship prediction and entity completion task is completed by applying a large model, so that the integrity and applicability of the knowledge graph are ensured. According to the method, a large amount of manually labeled data is not needed, the automation level and knowledge quality of knowledge graph construction are improved through the self-lifting thought of a large model and multi-time iterative optimization, and powerful support is provided for industrial equipment management, process optimization and intelligent diagnosis.

The invention provides a tunnel safety-oriented safety step pitch monitoring method and system, and the method comprises the steps: deploying an edge calculation module at a sensor node, carrying out the preprocessing and compression of collected monitoring data, reducing the data transmission amount through data dimension reduction and feature extraction, relieving the data transmission pressure, and improving the data transmission efficiency and real-time performance. An incremental learning algorithm is adopted to carry out online analysis on monitoring data, an anomaly detection model is dynamically updated according to a newly collected data sample, an early warning threshold value is adaptively adjusted, the accuracy and the real-time performance of anomaly recognition are improved, and misinformation and missing report are reduced; a multi-sensor data fusion technology is comprehensively applied, correlation analysis is carried out on sensor data of different types and different positions, the reliability and accuracy of monitoring data are improved through data redundancy and complementation, and comprehensive evaluation of the tunnel construction safety state is achieved.

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 provides a battery health state assessment method and system based on a BMS, and the method comprises the steps: presetting different voltage sampling ranges and resolutions for different load current intervals, judging the current interval to which the load current belongs when the load current changes, and dynamically switching to the corresponding range and resolution; when the current changes violently, the voltage sampling frequency is improved, more data points are obtained, and when the current changes gently, the sampling frequency is reduced, and data redundancy is reduced; filtering and fusing the internal resistance estimated value by adopting a Kalman filtering algorithm, dynamically tracking the change trend of the internal resistance, and adaptively adjusting the parameters of a filter according to the working state and the aging degree of the battery; in combination with working condition parameters including the temperature, the current and the SOC of the battery, a multiple regression model of the internal resistance and the working condition parameters is established, the multiple regression model is trained and optimized through a machine learning method, and online self-adaptive correction of the internal resistance is achieved.

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 belongs to the technical field of power prediction, and discloses a new energy power prediction method and system based on multi-source meteorological data fusion, and the method comprises the steps: obtaining meteorological data from different meteorological data sources in real time or at regular time through a data obtaining module; carrying out preliminary screening and preprocessing on the obtained data by utilizing a data processing module; the data fusion module is used for integrating meteorological data from different sources by adopting an advanced data fusion algorithm to eliminate data redundancy; a mathematical model or a machine learning model suitable for new energy power prediction is constructed by using the prediction model module based on meteorological data of multi-source fusion; and the result output and evaluation module is used for outputting the prediction result to a user in a visual form, and carrying out real-time or regular evaluation on the prediction result. The method has the characteristics of diversified data sources, advanced data fusion technology, flexible prediction model construction, high real-time performance and accuracy, wide application scenarios and the like.

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 discloses a railway office system heterogeneous database integration method based on a data conversion technology, and relates to the technical field of data integration, the method realizes seamless integration of heterogeneous databases in a railway office system by uniformly designing a route data standard model and combining an AI intelligent mapping algorithm; according to the scheme, a distributed architecture is adopted to process data conversion tasks in parallel, node loads, conversion speeds and parallel speed-up ratios are monitored dynamically, and the system stability in a high-load scene is ensured; the universal adaptation layer supports integration of multiple protocols and multiple interfaces, and the overall performance is effectively improved; meanwhile, a multi-level security mechanism and consistency verification are introduced in the scheme, dynamic encryption, authority management and data redundancy backup are combined, the integrity and security of data circulation are guaranteed, and the problems of complex data integration, poor compatibility and performance bottleneck are solved.

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 discloses a big language model knowledge graph construction method in the financial field, which performs relation extraction by introducing a deep learning algorithm, and integrates and verifies financial data from different data sources by using a field ontology and an inference engine. The problem of data integration caused by inconsistent data formats, entity redundancy and conflicts in a traditional method is solved, so that the accuracy and consistency of knowledge graph construction are greatly improved. According to the method, the problems caused by non-uniform names or data redundancy in different financial institutions, market reports and government files can be effectively eliminated, the calculation complexity is reduced, and the query efficiency is improved. And secondly, an intelligent question-answering system based on a natural language is introduced, so that the technical threshold of the user is reduced, and non-technical personnel can query and analyze financial data without mastering a complex query language.

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 belongs to the field of power equipment monitoring, and discloses an electrical signal acquisition system based on intelligent dynamic sampling, which comprises a signal acquisition module, a signal conditioning module, an abrupt change detection module, an impedance calculation module and an upper computer, and is characterized in that the signal acquisition module comprises a first ADC module, a second ADC module, a first MCU module and a second MCU module. Short-circuit impedance real-time calculation and waveform abrupt change detection are adopted for cooperative judgment, and when equipment operates stably, the sampling rate of the system is kept low; when impedance changes or waveform changes suddenly, the sampling rate is improved automatically, important signal changes are recorded accurately, and waveform abrupt change detection is carried out by adopting an edge detection algorithm improved based on an FIR (Finite Impulse Response) filter. The system ensures the time sequence consistency of data under different sampling frequencies through multi-channel data synchronization and timestamp management, and meanwhile, smooth transition is performed by applying an interpolation algorithm during sampling switching, so that the continuity of a data sequence is ensured. The data redundancy is reduced; and the integrity of key information is ensured.

The invention discloses a full-automatic three-dimensional live-action modeling method based on an unmanned aerial vehicle oblique photography technology, and particularly relates to the technical field of unmanned aerial vehicle photography, and the method comprises the following steps: firstly, dividing a modeling region into a plurality of unmanned aerial vehicle oblique photography regions, and presetting an initial flight route and a parameter combination; an initial image is collected according to a preset route, terrain and shelter analysis is carried out, and a region complexity level is output through fuzzy reasoning; determining whether the initial image is adopted or not according to the classification, and if not, re-collecting the image based on an optimization strategy; and finally, gathering and summarizing the adopted images, and inputting the collected images into three-dimensional modeling software to generate an accurate three-dimensional model. According to the method, the flight strategy of the unmanned aerial vehicle is dynamically optimized, and especially in complex terrains and shelter dense areas, the coverage degree and quality of image acquisition are ensured, so that the detail retention degree of a three-dimensional model is remarkably improved, resources of the unmanned aerial vehicle are more effectively utilized, unnecessary flight and data redundancy are reduced, and the modeling efficiency is improved.

The invention relates to the technical field of data management, in particular to a data asset sorting method, which comprises the following steps: preliminarily classifying all data assets in an enterprise, performing quality evaluation on the preliminarily classified data assets, generating a quality evaluation report, and performing data redundancy detection and optimization based on the quality evaluation result. Cleaning redundant data by adopting a deduplication algorithm and an optimization algorithm, carrying out data correlation analysis, generating a data correlation graph through a graph model and a statistical model, and determining the relationship between data sets; and finally, according to a correlation analysis result, integrating the data sets with high correlation into a unified data set by adopting a data fusion algorithm, and reducing a storage space through a data compression technology. According to the method, the accuracy of data classification and management can be improved, the data redundancy is reduced, the data storage and access efficiency is optimized, and the data security and privacy protection are enhanced through security evaluation and protection measures.

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 hierarchical storage, in particular to a photo-electromagnetic storage method and system based on hierarchical management, and the method comprises the following steps: collecting read-write speed, error rate, durability and cost data of optical storage and electromagnetic storage media, carrying out the sorting and grading processing of the data according to the read-write speed and the error rate, and carrying out the sorting and grading processing of the data; and performing score calculation on each storage medium to obtain a medium performance score. According to the method and the device, the data is effectively distributed to the most suitable storage medium by accurately scoring the performance of the storage medium and combining specific requirements of the to-be-stored data, so that the data access speed and efficiency are improved, the total storage cost is reduced, and particularly, when a large amount of data is processed, the data compression level and the encoding format are dynamically adjusted, and the data compression efficiency is improved. The storage efficiency of the data is further optimized, data redundancy is reduced, the overall performance of data storage and access is remarkably improved through optimization measures, and meanwhile long-term safety and integrity of the data are ensured.

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.