30 results about "Data fusion center" patented technology

The invention relates to the technical field of the Internet of Things, and discloses a campus smart life terminal comprehensive management and control system based on big data, and the system comprises a data collection module, a big data analysis module, a comprehensive management and control module and an execution feedback module. Operation data of different systems are integrated in real time, cross-platform data sharing and consistency of a canteen, an access control and an energy terminal are guaranteed, the problem of information isolated island caused by independent operation of terminal data is avoided, meanwhile, association analysis is carried out on collected environmental factors and user behavior information, the risk of terminal operation abnormity and resource conflict can be found in real time, and the user experience is improved. The method is advantaged in that high efficiency and reliability of terminal cooperation management are guaranteed, fault response delay is further reduced, a shunting scheme is automatically generated when sudden demand increase occurs, a condition of student waiting time prolonging is prevented, and dynamic adaptive capability and service quality of the campus life terminal are guaranteed.

The invention discloses an unmanned aerial vehicle-unmanned vehicle system collaborative target detection method and system and application thereof, and the method comprises the steps: integrating the high-altitude multi-modal data of an unmanned aerial vehicle and the ground three-dimensional point cloud / multi-view image of an unmanned vehicle through constructing a collaborative architecture of an unmanned aerial vehicle subsystem, an unmanned vehicle subsystem and a data fusion center; a deep learning algorithm is adopted to extract image features, Bayesian estimation is combined to fuse multi-source data, and posterior probability distribution of a target state is generated. And task allocation and dynamic path optimization are realized based on a path planning algorithm and a Hungary algorithm, and a detection strategy is adjusted through real-time communication. The system disclosed by the invention comprises a high-precision sensor, a communication module and a high-performance data processing center. According to the scheme, the target detection precision, the environmental adaptability and the efficiency in a complex scene are remarkably improved, and the method can be widely applied to urban security and protection, field exploration and logistics distribution.

The invention discloses a household traditional Chinese medicine robot which comprises a robot body, a face diagnosis acquisition module, a tongue diagnosis analysis module, an intelligent inquiry module, a pulse diagnosis detection module and a multi-modal data fusion center module. The system has the advantages that firstly, multi-mode traditional Chinese medicine diagnosis integration is achieved, intelligent fusion of four diagnosis modes of traditional Chinese medicine is achieved through collaborative analysis of face diagnosis, tongue diagnosis, pulse diagnosis and intelligent inquiry diagnosis, and the limitation of a traditional single diagnosis mode is broken through; 2, an accurate data acquisition technology: adopting advanced technologies such as five-color spectral imaging, tongue body dynamic tracking and a three-dimensional pulse pressure sensor to improve objectivity and accuracy of traditional Chinese medicine sign detection; and 3, dynamic personalized inquiry: based on the Bayesian network and the symptom knowledge graph, realizing real-time optimization of an inquiry path, and improving the dialectical efficiency. The method is suitable for household scenes, and the threshold of professional traditional Chinese medicine diagnosis is lowered.

The invention relates to the technical field of big data analysis, and particularly discloses a marketing management system based on big data analysis. The system comprises a data fusion center, a real-time portrait engine, a multi-contact attribution analysis module, a strategy optimizer and an execution gateway, and multi-source user data are integrated, a user interest model is dynamically updated, contact contribution weights are quantized, and marketing strategies are intelligently generated. Universe user view construction, real-time interest tracking, accurate attribution analysis and automatic marketing decision making are realized, and finally the marketing efficiency and the input-output ratio are improved.

The invention relates to the technical field of intelligent manufacturing and supply chain management, in particular to an intelligent management system for titanium plate inventory and production plan collaboration. Comprises: a multi-source data fusion center for a supply chain management system to obtain operation data and generate a collaborative situation original data set; the situation quantitative analysis unit is used for receiving the collaborative situation original data set and generating a market agility demand score and an inventory liquidity current situation score; the collaborative conflict evaluation unit is used for calculating a collaborative conflict index and determining a collaborative conflict situation level; the evolution trend prediction unit is used for generating an evolution sequence of future collaborative conflict indexes; and a dynamic decision management unit for generating an immediate conversion policy in response to the collaborative conflict context level and generating a predictive inventory structure pre-configuration decision. According to the method, the accuracy and objectivity of decision making are remarkably improved, and enterprises can deeply insight into operation core contradictions.

The invention discloses a digital operation management platform oriented to enterprise transformation and upgrading and a construction method thereof, and belongs to the field of enterprise digital transformation. The platform comprises a front-end system cluster which comprises a safety management module, a production management module, an asset management module, a material management module, a collaborative office module, a production real-time module, a public foundation module, a system management module and a data governance module. The construction method comprises the following steps: S1, constructing a unified data interface architecture in a data governance server, accessing a multi-source heterogeneous data source and caching an original data packet; s2, cleaning and converting data based on the unique field identifier, and generating a structured data table; s3, transmitting the data to a data fusion center node, performing partition storage, deconstructing semantics, and generating an index metadata set; s4, constructing a multi-path service route in the data service distribution server, and generating a static index table and a dynamic index flow; and S5, pushing the data to a front-end system cluster through an interface service layer, and calling the data by a user terminal to realize index-driven data view construction. The method has the beneficial effects that the data integration efficiency and the operation intelligence level are improved.

The invention relates to the technical field of curtain wall construction, and discloses a BIM and Internet of Things integration-based glass curtain wall construction operation and maintenance safety intelligent monitoring and early warning system, which comprises a sensing layer, a stress sensor array deployed on a curtain wall unit, a three-axis vibration sensor, a multispectral environment sensor, a high-definition track camera and a UWB positioning module, the edge layer comprises a Kalman filtering unit and a mixed compression unit based on LZW-Huffman, wherein the Kalman filtering unit and the mixed compression unit are integrated on a sensor terminal; the system comprises a cloud platform layer, a dynamic BIM engine, a multi-source data fusion center, a structure behavior knowledge graph database, a hierarchical decision maker and a feedback learning module. And an early warning execution layer. According to the invention, high-precision and low-delay data acquisition is realized through the sensing layer multi-source sensor array and the edge layer real-time filtering compression unit, the problem of data islands in the prior art is effectively solved, active prediction of failure conduction is realized through a real-time deduction engine and a weight adaptive module of a structure knowledge graph, and the reliability of the system is improved. The problem of weak risk prediction in the prior art is solved.

The invention discloses a highway-railway combined transportation scheduling method and system based on digital twinning and multi-agent cooperation. The method comprises the following steps: establishing a unique binding relationship of people, vehicles and goods in a transportation order based on an RFID binding algorithm, and synchronizing the unique binding relationship to a database; collecting railway transportation state data, road transportation state data and freight station operation data in real time; standardizing the railway transportation state data, the road transportation state data and the freight station operation data through a multi-source data fusion center; the connection time, the route and the parking position of the road vehicle are dynamically adjusted based on the LSTM collaborative scheduling model, and a road vehicle diversion and waiting duration estimation scheme under the emergency situation is output; railway carriage demands are predicted through LSTM, dynamic optimal matching of road trucks and railway carriages is dynamically matched based on a mixed integer programming model, an optimal matching scheme of the road trucks and the railway carriages is generated, unique binding of people, vehicles and goods is achieved by introducing an RFID technology, information barriers are broken, and real-time sharing and standardized interaction of data are achieved.

The invention discloses an expressway material supply early warning method based on meteorological data, and relates to the technical field of traffic engineering. Comprising the steps that a multi-source sensing module collects multi-dimensional information sources regularly, a data fusion center fuses the multi-dimensional information sources, a central processing unit establishes a congelation risk prediction model, a risk early warning module conducts congelation risk grading early warning, and an intelligent decision module conducts material supply scheduling optimization. According to the invention, the response time efficiency is greatly improved, the supply and demand matching precision is obviously improved, the purchasing efficiency and reliability are improved, the decision scientificity and cost are optimized, and the accident rate and economic loss are reduced; the problems of incomplete data, inaccurate prediction, one-sided evaluation, unbalanced scheduling and fuzzy response of the traditional highway anticoagulation and communication preservation material supply are solved, and the anticoagulation and communication preservation targets of accurate early warning, on-demand scheduling, controllable cost, safety and high efficiency are achieved.

The application discloses a kind of fusion display method and device of double platform data, method includes: when user requests to merge and show data on the first platform, obtain the platform identification and serial number sent by the first platform, obtain user account and organization identity based on platform identification through serial number to the first platform request, organization identity is bound with target organization identity in the second platform of user, obtain platform data through organization identity, two platform data are merged and calculated, obtain fusion data, and feedback to the first platform for display. Visible, after two platforms are associated by user account information and organization, user can obtain the organization identity of another platform with the bound organization identity in one of the platforms, without multiple login and selection identity, the data of two platforms can be fused by data fusion center, and then the fusion data of two platforms is shown on one platform, improve the experience of simultaneous use of double platform of user with demand.

The invention relates to the technical field of chip verification, and discloses a multi-agent-based large-scale chip verification method, device and equipment, and the method comprises the steps: obtaining the performance parameters of a chip based on a chip specification, and determining a verification target; creating a plurality of agents according to different emphasis of chip verification corresponding to the verification target; a distributed agent communication network is constructed based on the agents, so that the agents exchange data in real time; according to a chip design architecture and verification requirements, a large-scale chip verification task is decomposed into a plurality of sub-tasks, a task allocation algorithm based on task priorities and agent capabilities is adopted, the sub-tasks are allocated to corresponding agents for verification, the sub-tasks are uploaded to a data fusion center for comprehensive analysis, and a verification result is generated. According to the invention, the advantages of each agent can be fully exerted, and the comprehensiveness and accuracy of large-scale chip verification are improved, so that the requirement of rapid development of modern large-scale chips is met.

The invention relates to the technical field of intelligent management, and discloses a coal mine equipment intelligent transaction management system and method based on big data, and the system comprises an intelligent matching module, a transaction execution module, a financial risk control module and a data closed-loop module. A data fusion center is arranged, and when equipment supply and demand dynamic matching is carried out, a cross validation rule of a supplier productivity dynamic portrait and coal mine demand characteristics is constructed, and real-time cooperative calculation of multi-source heterogeneous data is supported, so that a system can identify logic conflicts between supplier parameter updating and purchasing demands in real time, the matching scheme deviation rate is eliminated, and the matching efficiency is improved. The transaction decision accuracy is ensured, the risk of resource mismatching caused by data islands is reduced, and the binding relationship between the delivery action and fund transfer is verified in real time by setting the block chain evidence storage module, so that the system can automatically freeze installment payment when the equipment acceptance is abnormal, the traceability period is shortened, and the efficiency is improved. The problem of credit loss caused by non-traceability of the transaction process is solved.

The application discloses a kind of based on digital twin and multi-agent collaborative highway-railway intermodal dispatching method and system, including the unique binding relationship of people, car, goods in transport order based on RFID binding algorithm is established, and it is synchronized to database;Real-time acquisition railway transport state data, highway transport state data and freight station operation data;The standardization processing of railway transport state data, highway transport state data and freight station operation data is carried out by multi-source data fusion center;Based on LSTM collaborative scheduling model, the connection time, route and stop position of highway vehicle are dynamically adjusted, and the highway vehicle rerouting and waiting time estimation scheme under the output of sudden situation are output;Railway carriage demand is predicted by LSTM, and the dynamic optimal matching of highway truck and railway carriage is dynamically matched based on mixed integer programming model, to generate the optimal matching scheme of highway truck and railway carriage, introduce RFID technology to realize the unique binding of people, car and goods, break information barrier, realize data real-time sharing and standardized interaction.

The application relates to the technical field of Internet of Things, and discloses a campus intelligent life terminal comprehensive management and control system based on big data, which comprises a data acquisition module, a big data analysis module, a comprehensive management and control module and an execution feedback module. Through the data fusion center module, different system operation data is integrated in real time during the comprehensive management and control of the campus life terminal, cross-platform data sharing and consistency of the canteen, the access control and the energy terminal are ensured, the information island problem caused by independent operation of terminal data is avoided, the collected environmental factors and user behavior information are analyzed in association, terminal operation abnormity and resource conflict risks can be found in real time, the efficiency and reliability of terminal collaborative management are ensured, fault response delay is further reduced, a shunting scheme is automatically generated when sudden demand increases, the situation that the waiting time of students is prolonged is prevented, and the dynamic adaptability and service quality of the campus life terminal are ensured.

Devices and methods are disclosed for in-network learning for dynamic wireless networks. For instance, a relay device (120a, b) for a wireless network (100) is disclosed, wherein the wireless network (100) comprises a plurality of edge devices (110a-n), each edge device (110a-n) configured to collect input data of one or more modalities of a plurality of modalities, one or more relay devices (120a, b) and a fusion center device (130). The relay device (120a, b) is configured to implement a relay machine learning, ML, model (121a-c) configured to aggregate encoded input data of a specific modality of the plurality of modalities into aggregated encoded input data of the specific modality, wherein the encoded input data of the specific modality is provided by one or more of the plurality of edge devices (110a-n) and wherein each of the one or more edge devices (110a-n) implements an encoder ML model (111a-n) configured to generate the encoded input data of the specific modality. Moreover, the relay device (120a, b) is configured to provide the aggregated encoded input data of the specific modality to the fusion center device (130) implementing a decoder ML model (131) configured to generate output data by decoding the aggregated encoded input data of the specific modality provided by the relay device (120a, b). The plurality of encoder ML models (111a-n) and the decoder ML model (131) are based on a first In-Network Learning stage of a first virtual wireless network, wherein the first virtual wireless network comprises a plurality of virtual edge devices implementing the plurality of encoder ML models (111a-n) for collecting input data of different modality and a virtual fusion center device implementing the decoder ML model (131). The relay ML model (121a-c) is trained with a second In-Network Learning stage of a second virtual wireless network comprising the plurality of virtual edge devices for collecting input data of the specific modality, each virtual edge device implementing the trained encoder ML model (111a-n), a virtual relay device implementing the relay ML model (121a, b), and a virtual fusion center device implementing a decoder ML model for input data of the specific modality.

This invention discloses a state update method based on relevant information age in an energy harvesting Internet of Things (IoT), comprising the following steps: establishing an evolution model of the network, energy consumption, and relevant information age in the energy harvesting IoT; based on the established IoT model, proposing a sensor state update optimization problem with the objective of minimizing the average relevant information age of the data fusion center; modeling the state update optimization problem as a partially observable Markov decision process with unknown sensor power; combining a flexible action commentator algorithm and a long short-term memory network, and proposing an action decomposition and mapping mechanism, designing a deep reinforcement learning algorithm to solve the established partially observable Markov decision process. The designed algorithm can effectively address the problems caused by unknown environmental dynamics, unobservable sensor power, and a large-scale discrete action space, and achieves the minimization of the average relevant information age of the data fusion center.

The invention discloses an iron and steel plant multi-mode monitoring data fusion system and a dynamic resource scheduling method based on an artificial intelligence internet of things. Comprising a multi-modal data acquisition layer which is deployed at ironmaking, steelmaking and rolling process nodes and transmits data to an edge calculation layer through an Internet of Things communication protocol; the edge calculation layer is used for fusing data features through a multi-modal attention mechanism to generate a primary fusion vector; data preprocessing and lightweight preprocessing are executed through a workshop-level gateway; the cloud data fusion center comprises a space-time alignment module, a multi-modal fusion engine and a dynamic resource scheduling module; computing resources are distributed based on the DRL model, emergency tasks with response delay smaller than 200 ms are scheduled to edge nodes, and complex tasks are unloaded to the cloud. The technical effect is remarkable, the multi-modal fusion accuracy is larger than 95%, the task delay is reduced by 40%, and the equipment fault prediction accuracy is larger than 90%.

The present application relates to the technical field of intelligent manufacturing and supply chain management, in particular to an intelligent management system for titanium plate inventory and production plan coordination. It comprises: a multi-source data fusion center for the supply chain management system to obtain operation data and generate a collaborative context original data set; a context quantitative analysis unit for receiving the collaborative context original data set and generating market agility demand points and inventory liquidity status points; a collaborative conflict evaluation unit for calculating a collaborative conflict index and determining a collaborative conflict context level; an evolutionary trend prediction unit for generating an evolution sequence of the future collaborative conflict index; and a dynamic decision management unit for generating an immediate conversion strategy in response to the collaborative conflict context level and generating a predictive inventory structure pre-configuration decision. The present application significantly improves the accuracy and objectivity of the decision, enabling enterprises to gain a deeper insight into the core contradictions of operations.

The invention discloses a false track identification method and system based on multi-radar detection, and relates to the technical field of radio navigation. The method comprises the following steps: deploying a data acquisition unit in a radar in a radar detection area, acquiring original trace point data of the radar in a preset monitoring period, transmitting the original trace point data of the radar to a data fusion center, and constructing a joint observation matrix of the radar detection area; then, steady-state observation relation analysis is carried out on the observation association graph sequence, a spatial steady-state feature library is dynamically updated, radar detection area track data generated by a data fusion center are collected, and the spatial steady-state feature library is combined to obtain a radar detection area track data fusion model; and finally, processing to obtain the type of the track of the radar detection area, and performing confidence feedback updating on the spatial steady-state feature library of the radar detection area, so that the track identification accuracy and reliability of multi-radar cooperative detection are improved.

The invention discloses an SVM (Support Vector Machine) cooperative spectrum sensing method and system based on PSO (Particle Swarm Optimization), and relates to the technical field of cognitive radio. The system at least comprises a cognitive user, an authorized user and a fusion center, an authorized user, namely a primary user (PU), transmits a signal in an authorized frequency band, and the signal is influenced by path loss and fading in a propagation process; a cognitive user is a secondary user SU, a plurality of SUs are randomly distributed in a network, energy sampling is periodically carried out on a target frequency band, and local observation data are obtained; the fusion center, namely, FC, receives observation data uploaded by each SU and is used for unified judgment; and a PSO module and an SVM judgment module are deployed at the fusion center. According to the method, a hyper-parameter automatic optimization module based on particle swarm optimization (PSO) is introduced into a fusion center, so that a kernel parameter and a penalty factor of an SVM classifier can be adaptively adjusted under the conditions of a dynamic channel and different signal-to-noise ratios, and a better compromise is obtained among detection precision, robustness and calculation efficiency.

The application discloses a kind of based on meteorological data's highway material supply early warning method, it is related to traffic engineering technical field, including multi-source perception module regularly collecting multidimensional information source, data fusion center carries out fusion to multidimensional information source, central processing unit establishes freeze risk prediction model, risk early warning module carries out freeze risk classification early warning and intelligent decision module carries out material supply scheduling optimization, the application makes that response timeliness greatly improves, supply-demand matching precision significantly improves, procurement efficiency and reliability improve, decision scientificity and cost optimization, and accident rate and economic loss reduce, through whole chain technical innovation, solve the problem of traditional highway anti-freeze and keep open material supply "data is not complete, prediction is not accurate, evaluation is one-sided, scheduling is unbalanced, response is fuzzy", realize "precise early warning, on-demand scheduling, cost controllable, safe and efficient" anti-freeze and keep open target.

The invention provides a comprehensive monitoring system for a hydropower station, which constructs a high-fidelity virtual model of power station equipment through a total-factor digital twin engine module, predicts the state of the equipment and provides situation pre-judgment capability. A deterministic transmission network which can be dynamically reconfigured is constructed by means of an intelligent elastic network architecture module, and differentiated service quality and transmission reliability are guaranteed. And the multi-modal data fusion center integrates and fuses multi-source heterogeneous data and a prediction result to generate unified panoramic situation awareness information, so that a comprehensive basis is provided for decision making. And the intelligent strategy generation module can form a distributed cooperative control strategy, so that the intelligence and cooperative efficiency of system control are improved. And the disaster recovery autonomous recovery module realizes rapid positioning and automatic recovery of system faults by using the strategy and the panoramic information. And finally, an intelligent resource scheduling module ensures efficient utilization of system resources and smoothness of overall operation. Therefore, comprehensive improvement of operation efficiency, safety and reliability is finally achieved.

The invention relates to a V2X joint resource allocation and task scheduling method based on perception-communication-computing integration, and belongs to the technical field of intelligent network connection traffic. In order to solve the problems of sensing-communication-calculation link separation, low resource utilization efficiency, prominent communication bottleneck and delay and the like in the prior art, the invention provides a unified joint optimization method. A vehicle integrated sensing and communication device collects environment samples and controls sensing errors; the scheduler selects a local computing or edge computing unloading mode according to a vehicle state and a channel condition; allocating bandwidth to the unloading task and uploading the unloading task to a road side unit for processing; performing air interface aggregation on the cooperative task to realize multi-vehicle data fusion; and the central scheduler applies an alternate optimization algorithm to jointly optimize the sensing sample number, the power, the calculation frequency, the bandwidth and the unloading proportion, and minimizes the task error or the total time delay under the constraint of the time delay and the energy consumption. According to the invention, cross-layer resource collaboration is realized, and the system perception quality and the resource utilization efficiency are effectively improved.

The application discloses a three-level distributed control system of an intelligent fire-fighting workstation based on an automatic driving chassis, wherein the first level is a cloud server layer, which is arranged in a remote data center and communicates with at least one fire truck through a mobile communication network; the second level is an edge computing layer, which is arranged on each fire truck and serves as a communication hub and a data fusion center between the fire truck and the cloud server layer; and the third level is a terminal device layer, which is arranged on a terminal device, the edge computing layer is connected with the cloud server layer and the terminal device layer respectively and serves as a data fusion and forwarding hub; the three-level distributed control system designed based on the application can realize the switching of the fire truck between an autonomous patrol mode and an autonomous fire extinguishing mode after discovering a fire, the network interruption and the network continuation function after network interruption, and the cooperative control of multiple fire trucks, and solves the technical problems of a data island, protocol heterogeneity, difficulty in considering real-time performance and intelligence, and network interruption vulnerability of an existing fire truck control system.

This invention discloses a sports health monitoring and early warning system based on a smart wearable device. The system includes: a multimodal sensor array that simultaneously acquires electromyography (EMG) signals, photoplethysmography (PPG) signals, and inertial measurement unit (IMU) data; a muscle-vascular coupling analysis module that calculates the muscle-vascular phase synchronization index to distinguish between central and peripheral fatigue; a hemodynamic causal inference module that constructs a multimodal temporal causal graph through conditional mutual information to achieve causal-driven early warning; a dynamic adversarial domain adaptive module that optimizes personalized models within a federated learning framework; an energy-information flow collaborative optimization module that dynamically schedules sensor sampling strategies; and a multimodal data fusion center that outputs a multidimensional health risk assessment vector using a graph neural network. This invention, through causal-driven early warning, adversarial domain adaptation, and energy-information flow collaborative optimization, can significantly reduce false alarm rates, improve personalized monitoring accuracy, and extend device battery life.

The invention belongs to the technical field of aviation rescue and overwater safety, and particularly relates to an amphibious aircraft overwater rescue method and system based on dynamic cooperative control. Through a real-time communication cooperation mechanism and a multi-role task synchronization model, integrated data sharing and instruction interaction of an airborne terminal, a lifeboat terminal and a shore-based / air-based data fusion center are realized, the current situation of information islands among subjects in traditional rescue is broken, cooperation delay and instruction chaos are avoided, and the rescue efficiency is improved. And the efficiency of multi-role linkage operation is obviously improved. The dynamic path planning algorithm based on RRT-MPC can automatically fuse multi-source information such as hydrology, meteorology, obstacles and the like, can generate a safe and efficient route without depending on artificial experience, can dynamically avoid dangerous areas especially in complex environments such as strong water flow, dense submerged reefs, low visibility and the like, and solves the problem that a traditional path is easy to deviate from the safe route.

The invention discloses a multidimensional perception and heterogeneous fusion method and device for a maritime object. A cooperative sensing network is formed by maritime robots such as an unmanned aerial vehicle, an unmanned ship and an underwater unmanned ship, the unmanned aerial vehicle and the underwater unmanned ship continuously sense an overwater part and an underwater part of a target in a plurality of time slots, the unmanned ship serves as a data fusion center and receives sensing data from the unmanned aerial vehicle and the underwater unmanned ship, and the unmanned aerial vehicle and the underwater unmanned ship are integrated. And fusing the received continuous time slot sensing data, and automatically adjusting the position of the unmanned aerial vehicle and the unmanned ship according to the positions of the unmanned aerial vehicle and the unmanned ship. And finally, the unmanned ship reconstructs a complete three-dimensional point cloud model of the maritime object through a radar sonar heterogeneous data fusion method. The invention provides an integrated system architecture with multidimensional sensing and cooperative scheduling capabilities for maritime affair sensing.

The invention relates to the technical field of settlement management, in particular to a settlement management multi-module integration and working system which comprises an environment sensing system, and the output end of the environment sensing system is connected with a data fusion center. The output end of the data fusion center is provided with an intelligent decision-making system, and the output end of the intelligent decision-making system is provided with an execution control system. Through cooperative operation of five modules including the environment sensing system, the data fusion center, the intelligent decision-making system, the execution control system and the feedback optimization system, comprehensive intelligence and automation of building management are realized, the system adopts a modular design, and through security and protection monitoring, equipment monitoring, energy management, video analysis and standardized interfaces, the system can be widely applied to the field of building management. Physical integration is achieved through a standardized interface, the flexibility and expandability of system deployment are ensured, the data fusion center innovatively combines data cleaning, multi-dimensional alignment and feature extraction technologies, and the processing efficiency and accuracy of multi-source heterogeneous data are effectively improved.

The invention discloses a rail infrastructure safety guarantee method, system and device and a storage medium, and relates to the technical field of rail traffic intelligent safety monitoring and control, and the method comprises the steps: collecting original rail data through a multi-source sensing module; processing the original orbit data by using a bilateral filter and a region growing method to obtain orbit data containing semantic tags; performing feature extraction on the orbit data through a data fusion center, and constructing a space-time correlation feature matrix; based on the track data, thermal-mechanical coupling simulation is carried out through a digital twinborn decision-making platform, and a simulation result is obtained; and based on the space-time correlation characteristic matrix and the simulation result, through an adaptive control module, generating an optimization control instruction and alarm information. According to the invention, the problems of fragmentization of a perception system, insufficient diagnosis decision intelligence and risk disposal passivity in related technologies are solved.

The invention relates to the technical field of ground vehicle navigation and positioning, in particular to an integrated navigation method integrating fault recovery and multi-task uncertainty estimation, and solves the problems that when an existing IMU / ODO integrated navigation system seriously distorts ODO data due to slipping, a traditional FDI method only removes and does not supplement the ODO data, the system is forced to be degraded into a pure inertial navigation resolving mode, and the navigation efficiency is low. The invention provides an integrated navigation method integrating fault recovery and multi-task uncertainty estimation, which has slip sensing and intelligent recovery capabilities, organically integrates three core links of'detection, replacement and fusion ', and comprises the following steps: data initialization, detection, replacement and fusion. ODO anomaly detection based on innovation is executed, multi-task pseudo odometer replacement data is generated, according to an ODO anomaly detection result based on innovation, an observation mode is dynamically selected, a measurement noise covariance matrix is constructed, IEKF is adopted as a data fusion center, and measurement updating and time recursion are carried out.