154 results about "Fusion center" patented technology

The invention discloses an AI agent design platform decision optimization method based on big data, and particularly relates to the field of artificial intelligence, comprising multi-modal data sensing layer construction, a streaming feature calculation engine, a dynamic index fusion center and an adaptive decision matrix. According to the method, accurate synchronous monitoring of the utilization rate of hardware resources and dynamic collaborative optimization of heterogeneous computing units are achieved, and the resource scheduling efficiency in a complex computing scene is remarkably improved; knowledge system degradation caused by long-term learning is effectively prevented, and the continuous reliability of a cognitive system is ensured. The provided multi-dimensional decision credibility verification system is fused with interpretability penetration analysis, environment coupling modeling and logic drift detection technologies, the limitation of a traditional single credibility index is broken through, the risk prediction and fault-tolerant capability of the decision process is remarkably enhanced, and a full-dimensional safety decision guarantee system is constructed for an intelligent agent.

The invention discloses a multi-source heterogeneous data fusion pipe network intelligent scheduling decision-making system, which comprises a multi-modal data acquisition cabin module, a space-time alignment fusion center module, a digital twin deduction cabin module, a self-adaptive decision-making matrix module, an elastic execution feedback chain module and a credibility tracing platform module, the multi-modal data acquisition cabin module comprises a heterogeneous protocol analysis unit, an unstructured processing engine and an edge preprocessing mechanism, and the space-time alignment fusion center module comprises a space-time reference mapping engine, a federal learning cleaning tower and a dynamic semantic association library. The problem that data of a traditional system cannot be effectively integrated is solved, fusion of multi-source heterogeneous data is achieved, data islands are broken, the response speed is increased, the dynamic response capacity is enhanced, in addition, decision making efficiency and accuracy can be improved, decision making intellectualization can be enhanced, optimal configuration of pipe network energy efficiency can be achieved, and the system is suitable for popularization and application. And the energy efficiency of the pipe network is greatly optimized.

Disclosed in the present invention are an age-of-sensing analysis and optimization method for a wireless power supply sensor network. The method comprises the following steps: S1, determining a network model; S2, determining a MAC layer model; S3, determining a channel model; S4, on the basis of a multi-segment formula, fitting a nonlinear energy-receiving model, and calculating the decoding success probability of a single sensor; S5, calculating the possibility of information being successfully received in a fusion center under a sensing information requirement threshold; S6, using a hypothesis testing theory to model a fusion-sensing reliability model, so as to obtain the probability of credible fusion; S7, by means of a joint sensing-transmission success possibility, acquiring a closed-form expression of age of sensing, and formulating an information timeliness optimization problem; and S8, respectively optimizing a sensor power supply duration, a fusion information demand threshold, and sensor deployment, so as to give a minimum age of sensing. By means of the present invention, age of sensing is optimized on the basis of the analysis thereof, thereby improving the timeliness of sensing information.

The invention discloses an urban rail transit management system based on multi-source data fusion, and the system is characterized in that a data collection module is connected with a data preprocessing module, and is used for collecting multi-source data of a power supply system, a vehicle system and a rail signal system; the data preprocessing module is connected with the data fusion center and is used for denoising, filtering and normalizing the received original data; the data fusion center is connected with the data acquisition module, the data preprocessing module and the decision analysis module and is used for realizing fusion of multi-source data; the decision analysis module is connected with the data fusion center and the control execution module and is used for performing fault prediction and emergency processing decision; the control execution module is connected with the decision analysis module and an external execution mechanism and used for controlling the execution mechanism to guarantee operation of the rail transit system according to instructions of the decision analysis module. According to the invention, operation efficiency and resource configuration can be improved, risks are reduced, shutdown time is reduced, safety and experience are guaranteed, maintenance and operation costs are reduced, and benefits are improved.

The invention discloses a steel coil storage yard checking method and device based on three-dimensional reconstruction, and relates to the technical field of computer vision, and the method comprises the steps: determining key frames based on the feature matching relation of each video frame of historical multi-view video data of a steel coil storage yard, and determining three-dimensional reconstruction data based on each key frame; the three-dimensional reconstruction data comprises imaging information, sparse three-dimensional point cloud data and dense three-dimensional reconstruction data; determining a three-dimensional cuboid bounding box enveloping the steel coil storage yard based on the imaging information and the sparse three-dimensional point cloud data, determining view angle imaging information based on the observation angle, and determining a historical multi-modal rendering graph based on the view angle imaging information and the dense three-dimensional reconstruction data; and processing the historical multi-modal rendering graph, training the initial storage yard inventory network model by using the obtained training set, and detecting the current multi-modal rendering graph by using the target storage yard inventory network model and a preset multi-view target result fusion center to obtain a storage yard inventory result. Therefore, the checking efficiency of the steel coil storage yard can be improved.

The invention discloses a help-calling emergency response system based on an intelligent communication network, particularly relates to the field of intelligent communication, and comprises a multi-source data acquisition layer, a preprocessing and feature extraction layer, a progressive analysis engine, a decision fusion center and an execution control layer. According to the method, the data integrity and the anti-interference capability are remarkably improved, the time-space reference unification module adopts a PTP protocol and a coordinate system conversion technology, accurate alignment of multi-source data is achieved, a multi-dimensional feature matrix is generated through time-frequency conjoint analysis, and the problem that the feature extraction dimension of a traditional system is single is solved; the progressive analysis engine triggers a hierarchical response strategy based on dynamic fusion of a transmission robustness index, a space confidence coefficient and an environmental risk value, combines terrain refraction compensation and a timeliness attenuation mechanism to optimize decision logic, breaks through the limitation of a fixed threshold response mode, adapts to communication quality fluctuation and environmental sudden change, reduces rescue delay risk, and improves rescue efficiency. And the resource scheduling accuracy and the system intelligence level are improved.

The invention discloses a power distribution method under cooperative tracking of a networking joint radar communication system, and the method comprises the steps: building a target state according to the position of a radar at each moment and a state vector of a target at each moment; establishing a measurement equation according to the measurement information of the target measured by the radar; establishing a communication model according to the distance between the radar and the fusion center and the reference path loss; a posterior Cramer-Rao lower bound of each target is constructed; constructing a power distribution model according to the posterior Cramer-Rao lower bound and the communication model; and solving the power distribution model by using convex optimization to obtain a power distribution result of the networking joint radar communication system. According to the method, radar tracking transmitting power distribution and communication power distribution are optimized, the posterior Cramer-Rao lower bound of target tracking is minimized on the premise that the lowest rate requirement of a communication system is met, the optimal balance of perception-communication efficiency is achieved, and the communication efficiency is improved. And the tracking capability of the networking joint radar communication system on the target is improved while the power waste is reduced.

The invention discloses a multi-feature-based cross-domain knowledge fusion system and method, and the system comprises an equipment layer and an edge intelligent layer which comprise an exclusive feature extraction tower and an equipment portrait engine, and carries out the compression of feature vectors through an auto-encoder or a full-connection network, and obtains a compressed feature package; the cross-domain fusion center is used for sequentially performing entity extraction, relation extraction, space-time alignment and knowledge fusion on the compressed feature packet and auxiliary data from other domains to obtain fusion data and storing / updating the fusion data; and the decision application layer comprises a display terminal used for displaying fusion data, and a scheduling unit used for providing a predictive maintenance scheme early warning unit and cross-device scheduling. According to the system provided by the invention, various types of sensors can be integrated, and more comprehensive and multi-dimensional data acquisition is realized; the richness and accuracy of the data are improved, and a firmer foundation is provided for subsequent feature extraction and knowledge fusion.

The invention discloses a communication network fault diagnosis method and related equipment based on multi-modal vector fusion, and the method comprises the steps: carrying out the vector structuring processing of historical heterogeneous telecommunication operation and maintenance data through a multi-modal vectorization engine, and obtaining a historical multi-modal vector set; performing vector fusion processing on the historical multi-modal vector set through a vector fusion center to obtain a historical multi-modal fusion vector; an alarm message queue in a telecommunication network is monitored through a diagnosis decision module, and if an unknown composite fault is detected from the alarm message queue, cosine similarity calculation is carried out on a current fault fusion vector corresponding to the unknown composite fault and a historical multi-mode fusion vector in a hierarchical storage module; and outputting a network fault diagnosis decision result. According to the method, the accuracy and comprehensiveness of network fault diagnosis can be improved, efficient data storage, retrieval and diagnosis decision are realized, the operation efficiency and diagnosis capability of the system are improved, and the method can be widely applied to the technical field of communication network operation and maintenance.

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 discloses a nuclear magnetism safety interval confirmation device, and the device comprises a quantum noise suppression module which is used for eliminating radar signal noise caused by the quantum tunneling effect on the surface of a metal object in an ultrahigh field intensity environment; the multi-sensor fusion center is used for integrating the radar cleaning signal, the real-time magnetic field intensity data and the terahertz penetrability detection result to generate fused object position information; the dynamic magnetic field reconstruction engine is used for calculating a magnetic field distortion gradient by analyzing Hall sensor array data and a magnetohydrodynamic model in real time; the nonlinear danger scoring module is used for receiving object distance, magnetic field gradient, existence time and object classification data; the hierarchical response system is used for triggering a corresponding response according to the danger score; and the cesium atomic clock synchronization module is used for eliminating a timing error caused by a time crystal effect through a block chain timestamp verification mechanism. According to the invention, risk closed-loop control in a high-field-intensity MRI environment is truly realized.

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 relates to the technical field of wireless communication, in particular to a low-frequency special data link communication system fusing a central networking and an ad hoc network. According to the technical scheme, the system comprises a central networking communication module, an ad hoc network communication module, a special data link module, a low-frequency communication optimization module, a clock synchronization module and a fault self-healing module. Through dynamic cooperation of the central networking and the ad hoc network, low-frequency signal special optimization, industry customization of a special data link and multi-module cooperation of precise clock synchronization and efficient fault self-healing, the problems of incomplete communication coverage, easy transmission interruption, poor industry adaptability and high information security risk in a complex environment are solved; the system is large in communication coverage range, stable in operation and accurately adaptive to different industry scenes, meanwhile, the real-time performance and safety of data transmission are guaranteed, the scene requirements of emergency rescue, industrial control monitoring, remote area communication guarantee and the like are met, and the stability and reliability of communication are effectively 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 a pile foundation concrete accurate pouring control system and method based on multi-source sensing fusion. The pile foundation concrete accurate pouring control system comprises a sensing layer and a control layer, wherein the sensing layer comprises a phased array ultrasonic probe, a binocular vision sensor and a laser ranging array; the modeling layer comprises a three-dimensional hole shape reconstruction module and a BIM digital modeling engine; the data layer comprises a real-time monitoring database and a multi-source data fusion center; the decision-making layer comprises a concrete dynamic evaluation model and a BIM visual platform; the execution layer comprises a self-adaptive PID controller used for adjusting the concrete pumping rate according to the decision instruction, and a three-dimensional visualization terminal used for displaying the BIM model with the tolerance zone in real time. According to the method, a BIM model containing dynamic compensation is constructed through ultrasonic three-dimensional scanning, closed-loop control is achieved by fusing multi-source sensing data, and tolerance band penetration early warning and self-adaptive PID adjustment are combined. The hidden defect discovery rate is improved, and the comprehensive construction efficiency is improved.

The invention belongs to the technical field of safe and efficient management of low-altitude airspace, and discloses a space-based collaborative awareness and communication method for low-altitude airspace management, which comprises the following steps during awareness: after a task collaborative dispatching center receives an airspace management demand, satellites in different orbits are dynamically dispatched to perform collaborative detection, and the space-based collaborative awareness and communication method is realized; the satellite carries a load for sensing; the detected data is directly preprocessed on a satellite; the processed multi-source data is sent to a fusion center for identification and fusion; generating a low-altitude real-time situation map; threat level evaluation; the invention discloses a near-real-time division method. Compared with an existing system depending on ground-based radar or pure space-based ADS-B, the system has the advantages that the system integration degree, the management precision, the information transmission timeliness and the like are remarkably improved, the main satellite and auxiliary satellite layout design is adopted, and the system can achieve full-coverage low-altitude monitoring of areas without ground facilities; the response efficiency is improved, the data volume downloaded by the satellite is remarkably reduced, the timeliness is improved, and the problems of weak processing and long time delay on the satellite are effectively solved.

The invention provides a multi-sensor fusion estimation method and system based on a token bucket traffic shaping mechanism, and belongs to the field of multi-sensor control. The problem of scheduling communication resources in existing multi-sensor fusion estimation and the problem of how to process state estimation and censoring measurement of a nonlinear system are solved. According to the method, a new communication protocol is designed, a trace of an upper bound of a local estimation error covariance and a trigger function designed based on a token are combined to serve as a judgment condition for whether transmission measurement is carried out or not, whether a measurement value of each node is transmitted to a local estimator or not is determined, and the characteristic of token traffic shaping is fully exerted; designing a local Tobit Kalman filter gain of each node by minimizing a trace of a filtering error covariance matrix; local estimates from each node are transmitted to a fusion center, global estimates are generated using federated fusion rules, and global estimates with appropriate weights are sent back to each node for prediction at subsequent local filters.

The invention belongs to the field of millimeter wave MIMOS system authentication, and discloses a distributed physical layer authentication method based on beam forming deviation, in the method, a DPLA framework makes a final decision based on a local decision of multi-direction perception generated by a plurality of partners by using beam mode deviation and a fusion center related to a hardware specific gain error, and the DPLA framework is used for carrying out multi-direction perception on the basis of the local decision of multi-direction perception generated by a plurality of partners. The low-complexity hybrid combination fusion rule adapts to the characteristics of a full-connection structure in a millimeter wave MIMO system to balance the calculation efficiency and performance, asymptotic detection and identification performance are systematically analyzed within a large-scale antenna range, a digital signaling matrix is designed through a deflection coefficient maximization principle to improve the authentication performance, and finally, the authentication performance is improved. The feasibility of the proposed framework is verified through comprehensive evaluation. Compared with a reference frame, the provided DPLA frame shows better robustness and efficiency.

The application provides a target positioning method based on multi-station long-time passive angle measurement, which is applied to an information collection node and comprises the following steps: obtaining angle information of a target collected by a passive sensor in a preset time period, and a sampling position and a sampling time of the passive sensor; estimating a first sampling position and a first motion speed corresponding to the passive sensor at a first time according to the sampling position and the sampling time of the passive sensor; estimating first angle information of the target corresponding to the passive sensor at the first time according to the angle information of the target collected by the passive sensor and the sampling time; and sending the first sampling position, the first motion speed and the first angle information corresponding to the passive sensor to an information fusion center, so that the information fusion center estimates the position and the speed of the target at the first time according to the first sampling position, the first motion speed and the first angle information corresponding to the passive sensor, and the communication amount can be reduced and the positioning precision can be improved.

The invention discloses a self-adaptive Byzantine attack defense method based on multi-mechanism cooperation, and relates to the technical field of radio networks. The method runs in a cognitive radio network comprising a primary user, a fusion center and a plurality of secondary users, and the core lies in that a self-adaptive closed-loop defense system is constructed through the synergistic effect of four core components including a dynamic reputation updating mechanism, a soft weight mapping mechanism, a sequential fusion and early stop mechanism and a lagging state machine; therefore, high sensing precision, high decision-making efficiency and high system stability are synchronously realized in a complex attack environment.

The invention relates to the technical field of data security, and provides a privacy protection method and device, equipment, a storage medium and a program product, and the method comprises the steps: carrying out the disturbance of collected original observation data based on a local differential privacy mechanism, and obtaining noise disturbance data; converting the noise disturbance data into privacy-removed representation data through privacy-removed projection, and sending the privacy-removed representation data to a fusion center for completing hypothesis detection of a user request; and the fusion center carries out hypothesis detection on privacy-removed representation data through a public hypothesis detection model and a private hypothesis detection model obtained through adversarial training. Through local differential privacy and privacy-removing projection, a confrontation training mechanism fusing a center public hypothesis detection model and a privacy hypothesis detection model is combined, so that a privacy protection mechanism can be optimized, privacy protection and data availability are balanced, the calculation overhead is reduced, and the real-time performance of data processing is improved.

The application relates to an interference identification method and device based on soft decision multi-node cooperative sensing. The method comprises the following steps: setting and identifying interference of a communication signal according to a classifier in a sensing node to obtain an interference identification result; inputting the interference identification result of each sensing node into a fusion center, processing all the interference identification results according to soft decision cooperative sensing to obtain a final interference identification result; quantitatively processing the interference identification result of each sensing node according to the fusion center, giving a label to a to-be-labeled interference sample according to the obtained quantitative result, inputting the final interference identification result and the label into the classifier in the sensing node through the fusion center for self-training to obtain a new trained classifier. The method can identify interference based on soft decision multi-node cooperative sensing, improves the accuracy of interference identification, and realizes high-quality self-training of the classifier through the fusion center, so that a new classifier with higher interference identification precision is obtained.

The application provides a convex optimization target positioning method for quantifying AOA, obtains original AOA measurements between sensor nodes in a sensor network, calculates Taylor series approximation processing arctangent function, quantizes original AOA measurement values, sends the quantized AOA measurement values to a fusion center after transmission through a non-ideal channel, transforms a target function into a convex function by using semi-positive relaxation, transforms and relaxes constraint conditions, transforms the problem into a convex problem, solves the convex optimization problem, and obtains an estimated value of a target position. The AOA measurement adopted by the application can still obtain the estimated value of the target position relatively accurately in a water acoustic sensor network positioning system with limited resources and limited energy, the convex optimization method is used to solve the target positioning problem, can converge to a global optimal solution and does not depend on the selection of an initial point, and has strong practicability and high accuracy.

The invention discloses an unmanned aerial vehicle formation cooperative positioning method based on stochastic analysis in a zero-trust scene. The method comprises the steps that each unmanned aerial vehicle calculates predicted motion state data; each unmanned aerial vehicle transmits the predicted motion state data to the updating stage of the own fusion center and the adjacent unmanned aerial vehicles; each unmanned aerial vehicle performs relative measurement on the adjacent unmanned aerial vehicles to obtain a measurement vector; each unmanned aerial vehicle detects the predicted motion state data transmitted by the adjacent unmanned aerial vehicle based on the measurement vector, and if the predicted motion state data is judged to be the predicted motion state data, subsequent operation is carried out; and each unmanned aerial vehicle performs data fusion updating on the predicted motion state data and predicted motion state data transmitted by the adjacent unmanned aerial vehicle based on the measurement vector to obtain state estimation in an updating stage. According to the invention, when the communication link of the unmanned aerial vehicle formation suffers from a malicious attack scene, a reliable and high-precision positioning result is provided for unmanned aerial vehicle formation control, and the flight safety of the unmanned aerial vehicle formation in a zero-trust scene is ensured.

The invention particularly relates to a weak target multi-station radar airspace accumulation method based on coherent receiving and transmitting channel matching, which comprises the following steps: a transmitting station transmits a linear frequency modulation pulse signal, and a receiving station receives a target baseband echo and performs pulse compression and discretization processing; the fusion center carries out time and space synchronization on the signals and then groups the receiving and transmitting channels in a search mode; coherent accumulation is adopted in the groups to maximize the cooperative gain, and non-coherent accumulation is adopted between the groups to avoid phase interference; and finally traversing the search unit and judging whether the target exists or not through threshold detection. The method can dynamically adapt to the echo correlation difference, achieves the optimal airspace accumulation gain, accurately detects a weak target in a low signal-to-noise ratio scene, greatly shortens the accumulation time, improves the early warning efficiency, is compatible with an existing multi-station radar architecture, and is high in engineering application value.

The invention discloses a multi-source measurement distributed cooperative positioning method based on gradient descent, and the method comprises the steps: predicting the prediction state information of a robot through a state space model, and transmitting the prediction state information to an adjacent robot and a positioning fusion center of the robot; obtaining multi-source measurement information by using a relative sensor and an absolute sensor, and constructing a relative measurement model and an absolute measurement model based on the multi-source measurement information; performing Kalman data fusion updating based on the multi-source measurement information and the prediction state information, and calculating absolute and relative Kalman gains; optimizing the absolute and relative Kalman gains by adopting a gradient descent algorithm to obtain optimal absolute and relative Kalman gains; and updating the state information of the robot by using the optimal absolute and relative Kalman gains. According to the invention, a multi-center distributed architecture is adopted, the optimal absolute and relative Kalman gains are solved by fusing multi-source measurement information and utilizing a gradient descent algorithm, and the technical problems of low positioning precision and stability in the prior art are solved.

The invention relates to the cross technical field of wireless communication and intelligent agriculture, in particular to a NOMA-SIC uplink weighting and rate maximization method based on an unmanned aerial vehicle (RIS), and the method comprises the steps: constructing an uplink system composed of a fusion center, an unmanned aerial vehicle reconfigurable intelligent surface and a sensor node, and enabling the fusion center to employ a continuous interference elimination technology, and realizing multi-user concurrent transmission based on power domain multiplexing of non-orthogonal multiple access. According to the method, fractional programming, quadratic transformation and successive convex approximation technologies are jointly used, phase configuration of the RIS and sensor transmitting power are alternately optimized, beam forming vectors are received by a fusion center, and under the condition that the power, phase and service quality constraint conditions are met, the system weighting sum rate is maximized. The advantages of NOMA spectrum multiplexing and SIC interference elimination are brought into full play, the transmission reliability and the spectrum efficiency are remarkably improved, and the method is suitable for low-delay and high-reliability acquisition of intelligent agricultural large-scale sensor data and can be expanded to the scenes of industrial Internet of Things, emergency communication and the like.

The application discloses a decision fusion method, device and system for wideband spectrum sensing. The decision fusion method comprises judging the type of cognitive user. If the cognitive user is a reference cognitive user, the original decision symbol sequence of the cognitive user is kept and sent to a fusion center so that the fusion center makes a fusion decision. If the cognitive user is a non-reference cognitive user, the original decision symbol sequence of the cognitive user is extended to obtain an extended decision symbol sequence. Then, the extended decision symbol sequence is subjected to segmentation and majority voting operation in sequence to obtain a compressed decision symbol sequence, wherein the compressed decision symbol sequence has the same length as the original decision symbol sequence of the reference user. Finally, the compressed decision symbol sequence is sent to the fusion center so that the fusion center makes a fusion decision. The application can ensure the reliability of spectrum sensing, significantly reduce the system sensing and transmission overhead, and improve the energy efficiency and realizability of cooperative spectrum sensing.