164 results about "Sensor cluster" patented technology

The invention discloses a power plant intelligent maintenance method and system based on multi-modal dynamic graph learning. The method comprises the following steps: acquiring structured sensor data, unstructured data and equipment physical topology data of equipment operation in real time through a multi-source sensor cluster and an industrial terminal; the method comprises the following steps: preprocessing multi-modal data, and fusing multi-modal features by using a double-flow Transform architecture and a gated attention mechanism to generate a joint embedded representation; constructing a dynamic causal graph based on equipment physical topology data and sensor time sequence characteristics, updating an edge weight through a GraphSAGE algorithm, fusing domain rule constraints, and outputting equipment state information; generating a maintenance strategy through an improved near-end strategy optimization algorithm according to the state and the equipment health index; and finally, the maintenance strategy triggers third-level early warning of the DCS through an OPC UA protocol, and a maintenance instruction is accurately issued. According to the method, the defects of a traditional method in the aspects of data fusion, fault modeling and decision making are overcome, and the safety, the economical efficiency and the operation and maintenance intelligent level of power plant equipment are remarkably improved.

The invention relates to the technical field of noise maps, in particular to a noise map dynamic deduction method and system based on multi-source heterogeneous data fusion, and the method comprises the steps: carrying out the space-time alignment of a multi-cluster low-dimension feature vector in an edge calculation node, and obtaining a multi-cluster low-dimension feature vector; generating a three-dimensional sound field reconstruction parameter of the first target data sequence and a spectrum energy distribution parameter of the second target data sequence; based on the three-dimensional sound field reconstruction parameter and the spectrum energy distribution parameter, dynamically controlling the sensor cluster to sleep and generating a space-time difference parameter; hierarchical encryption is carried out on the time-space difference parameters and the two types of data sequences in the dormancy period, and a basic ciphertext sequence and a feature ciphertext sequence for verification are generated; according to the method, the noise deduction map with space-time continuity is generated, high-precision dynamic deduction and energy efficiency optimization of the noise map are achieved, the evolution rule of noise along with time and space is effectively reflected, the real-time performance and accuracy of noise data are improved, and the actual situation of the noise can be reflected more truly, efficiently and in real time.

The invention discloses a three-dimensional cutting dynamic parameter adjusting system and method based on deep learning, and relates to the technical field of application of three-dimensional five-axis fiber laser cutting machines in the automobile manufacturing industry, and the three-dimensional cutting dynamic parameter adjusting system comprises a multi-mode sensor cluster, an edge calculation unit, a closed-loop control module and an AI model library, the multi-mode sensor cluster collects cutting data, the edge calculation unit receives the multi-mode sensor data and outputs dynamic adjustment parameters, and the closed-loop control module transmits the dynamic parameters to the laser generator and the five-axis motion controller through a high-speed communication bus to form a'sensing-decision-execution 'closed loop. The cutting state and risk are sensed in real time by designing a multi-mode sensor for collaborative decision, the problems that manual experience depends on and dynamic response lags are solved, the reject ratio is reduced, and the response speed is increased.

The invention discloses a track bridge health assessment method based on deep learning and multi-modal data fusion, and aims to overcome the technical defects of insufficient multi-source data collaboration and weak spatial-temporal characteristic coupling capability in the existing method. The spatial-temporal deep analysis of multi-modal data is realized by constructing a CNN-LSTM-DNN model. According to the technical scheme, the method comprises the following steps: 1) deploying acceleration, strain and temperature sensor clusters, and generating a space-time aligned heterogeneous data set through time synchronization, low-pass filtering and normalization processing; 2) extracting spatial topological features of a sensor network by using a CNN, and capturing a structure local deformation and load distribution mode; 3) analyzing a time sequence evolution rule of parameters such as bridge vibration and temperature change through LSTM, and establishing a long-term dependency relationship of dynamic load-environmental disturbance-structural response; 4) designing a weighted attention mechanism, fusing the spatial-temporal feature vector, and generating a comprehensive feature containing multi-physics coupling information, and 5) constructing a nonlinear mapping model based on DNN, outputting a comprehensive health index (CHI), and dividing damage levels.

The invention discloses a heat supply pipe network dynamic balance-oriented AI data brain management system, which comprises a data sensing module for synchronously acquiring temperature gradient distribution, pressure fluctuation time sequence and flow dynamic characteristics of a pipe network global region through sensor clusters deployed at heat source nodes, heat exchange stations and user terminals, and generating a multi-dimensional original data flow; the edge calculation engine is used for receiving the original data stream, executing a space-time alignment operation, fusing heterogeneous sensor data by adopting a multi-head space-time attention mechanism, and outputting a feature tensor with space-time relevance; and the topological reasoning module maps the feature tensor into a dynamic graph structure, and constructs topological representation including pipeline thermal resistance and node thermal capacity through a differentiable graph learning algorithm. Through a dynamic topology intelligent reasoning and multi-scale optimization cooperation mechanism, global dynamic hydraulic balance, high-efficiency regulation and control and strong disturbance self-adaptive response of the complex heat supply pipe network are achieved.

The invention relates to the technical field of offshore wind turbine inspection, and discloses an offshore wind turbine inspection monitoring system based on digital twinning. A twinborn data synchronization module of the system collects fan structure state data and environmental hydrological data in real time through a multi-source sensor cluster, and constructs an offshore fan digital twinborn body; the routing inspection path dynamic generation module analyzes a fan fragile area and risk distribution based on the digital twinborn body, and generates a multi-unmanned aerial vehicle cooperative routing inspection path in combination with real-time marine weather forecast; the unmanned aerial vehicle cooperative control module schedules an unmanned aerial vehicle group to execute a task according to the cooperative inspection path, and dynamically responds to real-time data updated by the twin data synchronization module; and the real-time abnormity identification module fuses and analyzes unmanned aerial vehicle returned data, identifies fan defects, outputs abnormal information, calculates relative space deviation, generates an attitude control instruction, and sends the attitude control instruction to the unmanned aerial vehicle cooperative control module to realize unmanned aerial vehicle attitude real-time adjustment.

The invention discloses a mining equipment energy consumption prediction method, and relates to the technical field of mining equipment energy consumption prediction.The mining equipment energy consumption prediction method comprises the steps that through multi-dimensional data collection, operation, process and environment parameters are collected through a sensor cluster; noise reduction is carried out through a generative adversarial network in combination with empirical mode decomposition, and data is restored through a space-time interpolation network; identifying working conditions and extracting features by means of a hidden Markov model in combination with an attention mechanism; a cross-device transfer learning framework is constructed, and a cloud training general model is combined with local data fine tuning; the edge end deploys a lightweight model for real-time prediction, and the cloud end generates a global energy-saving strategy; through digital twinborn visualization, a model and a strategy are automatically corrected based on SHAP value analysis. The equipment idling rate is reduced; the unit energy consumption of the crushing link is reduced; the abnormal response time is shortened; and the prediction precision and the system adaptability are remarkably improved.

The invention discloses a control system for improving the numerical control machining precision of a bionic unmanned aerial vehicle wing, and relates to the professional technical field of numerical control machining, and the control system specifically comprises a multi-dimensional sensing module, a digital twin model construction module and a numerical control machining precision improvement module, the multi-dimensional sensing module acquires geometric and material states and dynamic response data through a high-precision sensor cluster and realizes space-time consistency, the digital twin model building module comprises geometric modeling and physical attribute modeling, and is used for predicting the service life of a tool by combining an Archard wear model based on laser scanning point cloud and CAD (Computer Aided Design) model registration compensation pose deviation. And meanwhile, a five-axis machine tool multi-body dynamic model is established, a numerical control machining precision improving module dynamically divides sub-routes, establishes an error prediction model, corrects a machining path in real time, regulates and controls monitoring frequency according to geometric errors, thermal errors and material damage errors, triggers a cooling system to enhance or bypass a damage area, and guarantees machining precision.

Systems and methods for battery system management and monitoring apparatus(es) and equipment using a sensor cluster are described. The systems and methods can be used to automatically repair or otherwise address actual and predicted failure modes of the apparatus(es), which include electrical systems, power systems, energy storage systems, and other systems.

The invention discloses a BIM building intelligent integrated management system based on Internet of Things control, and relates to the technical field of networking technology and building intelligent management, and the system comprises a sensing layer, a transmission layer, a platform layer and an application layer. The sensing layer comprises a multi-source heterogeneous sensor cluster and is divided into an environment monitoring unit, an equipment state acquisition unit, a personnel positioning unit and a structure health monitoring unit, and each unit is provided with a corresponding sensor; the transmission layer adopts a LoRaWAN and 5G fusion architecture and comprises an edge computing gateway, a TSN switch and a data security encryption unit, and the gateway can perform frequency domain analysis and the like; the platform layer is based on micro-service and is provided with a multi-source data fusion engine and a BIM model dynamic updating module; the application layer provides an equipment fault prediction module, an energy optimization module and a safety risk early warning module, and all the modules adopt corresponding algorithms. According to the invention, a hierarchical collaborative architecture is constructed, a multi-source sensor, a heterogeneous network and the like are integrated, deep integration of multi-dimensional management of a building is realized, and the problems of subsystem independence and data obstruction of a traditional system are solved.

The invention discloses a sensor-based agricultural information data acquisition system and method, and belongs to the technical field of agricultural information acquisition. Multi-type sensor nodes are arranged in an agricultural target area in a heterogeneous manner; the method comprises the following steps: constructing a multi-dimensional influence factor model according to crop growth stages and environmental historical fluctuation data, dividing initial sensing sub-regions, and configuring a sensor cluster; dynamically updating the sensing boundary based on the historical change rate and the spatial gradient information; fusing the heterogeneous data by adopting a multi-channel time synchronization mechanism to generate a standardized environment vector set W; calculating a sampling priority matrix according to a parameter change trend in the W, and adaptively adjusting a node state; integrating an energy consumption estimation model, and executing low-power-consumption scheduling; when any parameter exceeds the threshold, high-density sampling and remote early warning are triggered; according to the method, high-precision, low-power-consumption and dynamic-response data acquisition and intelligent early warning can be realized, and the efficiency and reliability of an agricultural sensing system are improved.

The invention discloses an assembly equipment health management system based on industrial Internet of Things, and particularly relates to the field of intelligent maintenance of industrial equipment, comprising a distributed sensing module, a cross-production-line transfer learning module, a dynamic maintenance strategy optimization module, a multi-modal man-machine cooperation module and an enhanced execution terminal module, multi-source sensor clusters and intelligent edge nodes are deployed, multi-dimensional operation data are collected and preprocessed at high frequency, an encrypted global fault feature library is constructed by using a federal transfer learning algorithm, cross-production-line group intelligent advanced early warning is realized, and the equipment health degree, the production plan and the resource state are comprehensively considered based on a deep reinforcement learning decision-making agent. A priority maintenance strategy is output, a digital twinning and AR technology is fused to generate an enhanced maintenance guidance package, real-time operation verification and health re-evaluation are realized through an execution terminal, accurate and efficient closed-loop health management is formed, and the equipment reliability and the maintenance intelligence level are remarkably improved.

The invention relates to the technical field of heat pump system control, in particular to a heat pump system control method and system based on compressor rotating speed feedback. The method comprises the steps that a refrigerant side parameter cluster, an air side parameter cluster and a heat load side parameter cluster of the heat pump system in a preset sliding window are obtained, and a first refrigerating capacity, a second refrigerating capacity and a third refrigerating capacity are determined through the different parameter clusters so as to determine fault source positioning indexes corresponding to the different parameter clusters; comparing the fault source positioning index with a preset fault threshold value, and determining a fault sensor cluster from a refrigerant side parameter cluster, an air side parameter cluster and a heat load side parameter cluster; and controlling the rotating speed of a compressor of the heat pump system according to a determination result of the fault sensor cluster. According to the technical scheme, the rotating speed of the compressor of the heat pump system can be effectively controlled.

The invention relates to the field of mine risk prediction, in particular to a mine dynamic risk prediction method and system based on multi-modal parameter fusion analysis. The method comprises the following steps: obtaining a multi-modal mine omnibearing data stream based on a multi-modal sensor cluster, carrying out adaptive filtering noise reduction and time-space synchronization space mapping, and constructing a cross-modal mine data stream model; performing mine geological micro-fluctuation behavior deep analysis according to the cross-modal mine data flow model, performing dynamic geological risk situation evolution, and constructing a multi-modal geological risk situation map; and obtaining a mine historical risk event log, performing risk event extraction, performing adaptive risk chain deep learning on the multi-modal geological risk situation map, and constructing a mine risk state dynamic evolution model. According to the invention, intelligent prediction and dynamic early warning of mine risks are realized, and the mine intrinsic safety level and the emergency response efficiency are improved.

The invention discloses an environmental event dynamic risk assessment method and system based on a Bayesian network, belongs to the technical field of artificial intelligence, and aims to solve the technical problems that in existing environmental risk assessment, dynamic data adaptability is poor, multi-source heterogeneous data fusion is difficult, and real-time performance is insufficient. Comprising the following steps: acquiring environment data through a sensor cluster deployed in an environment, and uploading the environment data to an edge computing node; performing data preprocessing on the environmental data through the edge computing node; extracting time series data fragments from the standardized data stream based on a predefined sliding time window; constructing a risk prediction model based on the dynamic Bayesian network; and taking the extracted time series data fragments as input, performing risk level analysis through a risk prediction model in combination with particle filter reasoning, predicting and outputting a risk level, a risk level probability value and a risk conduction path as prediction results, and constructing a visual risk conduction map based on the prediction results.

The invention relates to the technical field of soil layer or rock drilling, in particular to a well drilling well control intelligent early warning system and method.The system comprises an acquisition unit, an establishment unit, an identification unit, an analysis unit, an evaluation optimization unit and an early warning generation unit; and performing multi-source data fusion analysis by combining edge calculation and a cloud platform, generating an optimized risk assessment result, and outputting a well control early warning instruction. According to the invention, the precision and real-time performance of well control risk monitoring can be improved, the problems that a traditional sensor is susceptible to interference and slow in response speed are effectively solved, and intelligent safety guarantee is provided for drilling operation.

The invention discloses a ship bulwark drainage intelligent management system combined with multi-sensor data monitoring, and relates to the technical field of ship intelligent control and automation, and the ship bulwark drainage intelligent management system comprises a sensor group, a dynamic trust chain fusion decision module, a multi-modal cross validation module, a fault-tolerant execution and reverse verification module and a central controller. According to the ship bulwark drainage intelligent management system combined with multi-sensor data monitoring, by constructing a dynamic trust chain fusion decision model and a multi-modal cross validation mechanism, the accuracy and timeliness of multi-source sensor data fusion under complex sea conditions are improved; sensor misjudgment caused by mechanical vibration, bubble interference and the like is effectively suppressed, the false triggering rate of a drainage instruction is reduced and response delay is shortened in combination with digital twin rehearsal and a reverse verification closed loop, and cooperative fault tolerance of a sensor cluster is realized through two-stage screening and a majority voting mechanism. And continuous and stable operation of the system under single-point fault or abnormal interference is ensured.

The invention relates to the field of mines, in particular to a metal mine underground filling body quality intelligent monitoring system and method based on the Internet of Things. Comprising the following steps: acquiring original measurement values, automatically calibrating the received original measurement values by using a sensor cluster mutual verification technology based on Kalman filtering, and only performing mutual verification repair on the original measurement values which are detected to drift and fail to obtain calibrated measurement values; fusing the calibrated measurement values of the same type of sensors to obtain fused data, and generating a low-dimensional feature vector; and predicting the risk probability of the quality of the underground filling body of the metal mine through a feature selection weighted random forest prediction algorithm, comparing the risk probability with a risk threshold value, carrying out graded early warning to obtain an early warning level, and generating a corresponding reinforcement scheme. The technical problems that an underground sensor is affected by environmental interference or equipment aging, a measured value is prone to drifting and failure, and a traditional method lacks an automatic calibration mechanism, needs manual intervention and is low in efficiency are solved.

The invention provides a hydraulic motor working state detection method and device and a storage medium, and relates to the technical field of fault detection.The method comprises the steps that a sensor cluster is arranged at key nodes of a hydraulic motor, and a multi-dimensional monitoring data set is constructed; calculating displacement data of a hydraulic motor and kinematic viscosity data of hydraulic oil according to the multi-dimensional monitoring data set to obtain a global monitoring data set; the actual leakage amount of the hydraulic motor is calculated based on the global monitoring data set, and an operation mode switching instruction is generated; receiving the load monitoring instruction or the shutdown maintenance instruction, starting multi-mode fault diagnosis, and obtaining a fault cause set of the hydraulic motor by using a Bayesian network model; and determining a maintenance scheme according to the fault cause set of the hydraulic motor. The technical problems that in the prior art, hysteresis exists, effective preventive detection measures are lacked, potential problems possibly existing in a hydraulic motor cannot be found in time, the maintenance cost is high, and the equipment recovery difficulty is large are solved.

The invention discloses a building construction safety monitoring system and a monitoring method thereof. The building construction safety monitoring system comprises a sensing layer, a transmission layer, a data processing layer and an application layer. The sensing layer is composed of multi-dimensional sensor clusters of environment, structure, personnel and the like and can comprehensively collect various data of a construction site; the transmission layer adopts a wireless and wired combined mode, and ensures stable and safe transmission of data through encryption and redundancy transmission technologies; the data processing layer is deployed on a cloud platform, and performs data preprocessing, model training and risk decision making by using multiple algorithms; the application layer provides a monitoring center client and a mobile terminal APP for a user, and realizes data display, early warning and management functions. The system is high in intelligent degree, achieves the omnibearing, real-time and precise monitoring of the building construction process, improves the accuracy of safety risk assessment and the timeliness of early warning, provides a powerful guarantee for the safety of building construction, and has a higher practical value and a higher popularization prospect.

The invention relates to the technical field of large models, and particularly provides a large model data enhancement method and device, and the method comprises the following steps: S1, collecting original data through a sensor cluster, grouping the data through a Pearson's correlation coefficient, and for each group, determining a head sensor through employing a minimum spanning tree algorithm, and building a generation sequence; s2, starting a generator based on a long short-term memory (LSTM) network, and training data of a head sensor by using the LSTM generator; s3, starting a ridge regression generator, and training data of other sensors by using the ridge regression generator; and S4, generating corresponding data by using the model obtained by training according to a specified demand of a user, and increasing the data volume through interpolation. Compared with the prior art, the method has the advantages that inherent complex time behaviors in the industrial production process can be learned, the relation between sensors which is crucial to accurate simulation process conditions is kept, and it is ensured that the generated data mode has high fidelity while the original process characteristics are kept.

The invention relates to a household equipment intelligent fault early warning and after-sales optimization system based on multi-sensor fusion and wireless transmission, which comprises a sensing layer, a transmission layer, a cloud intelligent layer and an application layer, and is characterized in that the sensing layer comprises a heterogeneous sensor cluster and an edge computing node, and the heterogeneous sensor cluster deploys a multi-dimensional sensor; and mechanical, electrical and environmental parameters are covered. Through a physical model and data-driven double-engine architecture, a flexible sensing technology and a decentralized service network, full-chain intelligent reconstruction from fault early warning to after-sales service is realized, and analyzed data is timely and stably transmitted back to an after-sales service center or a specified monitoring platform. Through deep analysis and processing of the sensor data, a scientific fault prediction model can be established, and the model can be utilized to accurately predict possible faults of the equipment and perform early warning before the faults occur, so that a user can know potential problems of the equipment in advance and take corresponding measures in time.

The invention discloses a box-type substation transformer fault diagnosis method and system based on artificial intelligence, relates to the technical field of power equipment fault diagnosis, and solves the problems that a sensor mechanism lacks a dynamic redundancy and verification mechanism, the feature contribution degree difference is not considered in cloud feature fusion, and the fault diagnosis efficiency is low. The characteristic quality is difficult to guarantee; and the diagnosis model architecture is single. Comprising the following steps of firstly constructing a sensor cluster network, dividing a main unit, a standby unit and a verification unit through a multi-index dynamic weighting algorithm, performing cooperative work, then generating a lightweight feature set through edge node preprocessing, constructing a normalized multi-dimensional feature matrix through cloud data integration, and finally constructing an edge-cloud secondary diagnosis architecture. The edge quickly screens the fault through a random forest, and the cloud end dynamically adjusts the feature fusion proportion by adopting a double-branch fusion model in combination with a gating circulation unit, and outputs the fault type and confidence.

The invention relates to a transformer monitoring method and system based on a sensor network technology. The method comprises the following steps: acquiring an initial physical parameter matrix collected by a heterogeneous sensor cluster corresponding to a target transformer; based on the distribution height of each sensor and the sensing node distance in the heterogeneous sensor cluster, performing space-time normalization processing on the initial physical parameter matrix to obtain a target physical parameter matrix of the target transformer; and determining the operation state of the target transformer according to the target physical parameter matrix of the target transformer. The heterogeneous sensor cluster includes a plurality of different types of sensors. By adopting the method, the accuracy of transformer state monitoring results can be improved.

The invention discloses an intelligent feeding, packaging and sorting system and method based on high-precision visual identification, and the method comprises the steps: a sensor cluster carries out the all-dimensional scanning of a to-be-sorted object, and collects multi-dimensional data; the data is processed in real time through an edge calculation module, and position information, category information and size information of the article are generated; identifying and classifying the articles based on a deep learning model, and if the articles with irregular shapes or damaged surfaces are identified, adjusting an identification strategy through a self-adaptive algorithm; according to the identification result of the articles, a correct path is selected through the intelligent sorting system, and the articles are conveyed to a designated sorting area; according to the size, the shape and the type of the article, a packaging mode is automatically adopted, and the article is packaged through an automatic packaging system; and after being packaged, the articles are conveyed to a delivery area to be distributed or further processed.

The invention relates to the technical field of intelligent automobiles, in particular to an intelligent cabin multi-AI Agent cross-domain collaborative super-brain system and an interaction method, and the system comprises a sensor cluster, a central decision AI Agent, a five-domain AI Agent and a cross-domain synchronization mechanism. The sensor cluster is used for collecting multi-dimensional data and transmitting the multi-dimensional data to the central decision AI Agent; the central decision AI Agent is used for processing the data, including analyzing fused data to establish a mapping relation corresponding to scenes and services, performing cross-domain priority arbitration and realizing inter-domain data synchronous scheduling; the five-domain AI Agent comprises a cabin domain AI Agent, a power domain AI Agent, a vehicle body domain AI Agent, a chassis domain AI Agent and an automatic driving domain AI Agent and is used for executing specific functions. According to the invention, the problem of function islands of a traditional intelligent cabin is effectively solved, deep coupling and dynamic linkage of cross-domain functions are realized, the function utilization rate is increased from less than 20% in the prior art to more than 78%, and the utilization rate of system resources is remarkably improved.

The invention relates to the technical field of mine safety production, in particular to a mine ventilation parameter dynamic optimization method based on industrial internet of things, which comprises the following steps: S1, data fusion perception and edge calculation; s2, modeling a digital twin ventilation network; s3, performing multi-objective optimization solution; s4, instruction issuing and feedback; and S5, model self-correction and knowledge base updating. According to the scheme, the multi-dimensional sensor cluster and the dual-mode communication network are constructed through the industrial Internet of Things, real-time sensing and differential transmission of mine ventilation parameters are achieved, the data cleaning and feature extraction technology of edge calculation is combined, the authenticity and effectiveness of input data are ensured, and true and effective data are provided for dynamic optimization of the ventilation parameters.

The invention relates to the field of vacuum degree compensation, in particular to an intelligent vacuum degree compensation method based on temperature change driving, and the method comprises the steps: arranging an integrated sensor cluster on the inner wall of a falling body cavity, collecting a temperature initial value and a vacuum degree initial value of the falling body cavity when a to-be-monitored falling body is in a static state, and collecting the temperature initial value and the vacuum degree initial value of the falling body cavity when the to-be-monitored falling body is in a falling body state; the method comprises the following steps: collecting real-time temperature data and real-time vacuum degree data of a falling body cavity at preset time nodes at intervals, calculating an operation difference value under the time nodes, carrying out training preprocessing, constructing a deviation monitoring model to learn operation pre-training data, obtaining a deviation fluctuation map, extracting a fluctuation value, comparing the fluctuation value with a fluctuation threshold value, and obtaining a deviation monitoring result. The falling acceleration calculation and / or vacuum degree intelligent compensation are / is performed on the falling body cavity, so that the problem of vacuum degree deviation accumulation caused by incapability of real-time monitoring is avoided, the vacuum degree in the falling body cavity is always kept in a relatively accurate range, intelligent decision of vacuum degree compensation is realized, and the automation degree of vacuum degree compensation is improved.

The invention discloses a signal positioning method, electronic equipment and a computer readable storage medium, and the method comprises the steps: collecting first signal data through a positioning system; acquiring second signal data through a multi-source data sensor cluster; wherein the first signal data and the second signal data are from the same signal source; the second signal data comprises various sensor data; performing weighted fusion processing on the first signal data and the second signal data; and positioning the signal source by using the data after the weighted fusion processing. Through the above method, the precision and reliability of positioning can be improved, the anti-interference capability is enhanced, and high-precision positioning can be provided in complex scenes and interference scenes such as cities, canyons, tunnels and the like.

The invention relates to the technical field of veterinary epidemic disease detection, and discloses a veterinary epidemic disease remote monitoring and intelligent diagnosis system, which comprises a multi-mode biosensor array including an implantable miniature biochip, a wearable flexible physiological monitoring patch and an environment sensing sensor cluster, the implantable miniature biochip is used for being implanted into a key tissue organ area in an animal body and collecting microscopic physiological data such as cellular metabolism signals and local immune reaction marker concentration in real time, and the wearable flexible physiological monitoring patch is used for being attached to the body surface of the animal. By means of the multi-mode biosensor array and the edge intelligent preprocessing unit, animal physiology and environment data are accurately monitored in real time. The multi-modal biosensor array covers the implantable miniature biochip, the wearable flexible physiological monitoring patch and the environment sensing sensor cluster, and can comprehensively collect in-vivo microscopic physiological data, body surface macroscopic physiological parameters and feeding environment information of animals.