313 results about "Decision fusion" patented technology

The invention discloses a wind power plant booster station multi-source data fusion anti-misoperation locking intelligent decision-making and early warning method, and relates to the technical field of intelligent misoperation prevention of a power system, and the method comprises the following steps: collecting multi-source heterogeneous data, obtaining the data through a distributed sensor network, and carrying out the edge calculation preprocessing; performing data space-time alignment and fusion, performing equipment state evaluation, and constructing a deep belief network and Bayesian network hybrid model to calculate a health index; anti-misoperation rule modeling is carried out, and operation logic verification is carried out based on a Petri network and an expert knowledge base; risk early warning decision making: fusing multi-source early warning information to divide risk levels; intelligent locking control is carried out, and a locking strategy is optimized through reinforcement learning; and performing decision support and visualization, constructing a three-dimensional digital twinborn model, and displaying operation guidance and risk early warning in combination with an AR technology. Through multi-source data fusion and intelligent decision making, the anti-misoperation locking accuracy and efficiency are improved, and the safety and the operation and maintenance level of the booster station are remarkably enhanced by equipment fault early warning three months ahead of time.

The invention discloses an intelligent management method for liquidation receipts based on multi-modal data fusion, and particularly relates to the field of data analysis. Comprising the steps of S1, multispectral data acquisition in a limited illumination environment, S2, cross-modal feature decoupling and recombination, S3, space-time heterograph neural network analysis, S4, multi-scale attention decision fusion, S5, resistance enhancement verification, and S6, incremental management based on knowledge distillation. According to the method, the physical anti-counterfeiting capability is remarkably improved, the paper material, the ink components and the surface structure are deeply analyzed through a multispectral sequence acquisition mechanism, and hidden tampering behaviors such as color fading and chemical altering of the thermo-sensitive paper are accurately identified. Cross-modal deep correlation analysis is achieved in a breakthrough mode, a physical-semantic decoupling technology and a space-time heterogeneous graph network are adopted for modeling, and non-dominant laws such as commodity position offset and tax rate anomaly are effectively captured.

The invention discloses a cardiovascular disease risk prediction method and system based on dietary multi-modal data and ensemble learning, and the method comprises the steps: constructing a multi-modal set through integrating multi-source heterogeneous data such as demographic statistics, dietary nutrition, clinical physiological and biochemical indexes and lifestyles; data cleaning is completed based on a box plot method and missing value processing, and key features are screened through Pearson's correlation coefficients, variance expansion factors and feature importance evaluation; a plurality of heterogeneous base models are fused by adopting a Stacking integration framework, a meta-feature matrix is generated through five-fold layered cross validation, and multi-level decision fusion is realized through a logistic regression meta-model; and quantifying the contribution weight of the dietary characteristics to the risk in combination with an SHAP method, and generating a visual interpretation chart and personalized intervention suggestions. According to the method, the accuracy and the stability of a prediction result are remarkably improved, the contribution degree and the action mechanism of dietary factors and other characteristics to the prediction result can be deeply analyzed, powerful support is provided for accurate prevention and personalized treatment of cardiovascular diseases, and the method has good practical value.

The invention relates to the technical field of enterprise management, in particular to an intelligent decision support system and method based on cognitive logic and scenarized semanteme, and the method comprises the steps: obtaining enterprise decision data through multi-source data monitoring, carrying out the preprocessing, and generating cross-modal enterprise scene cognitive information; analyzing cross-modal association among the data, fusing a cognitive logical reasoning rule and a semantic understanding model, and constructing scenarized semantic decision fusion features; training an enterprise decision-making quality evaluation model based on historical cases, performing quality perception on a current decision-making scene, and outputting decision-making quality information; and an execution effect is judged in combination with an expected target, an optimization mechanism is started if the target is deviated, candidate schemes are generated by using a historical knowledge base and a multi-target optimization algorithm, an optimal solution is screened, and intelligent adjustment of a decision scheme is realized. According to the method, multi-source heterogeneous data and cognitive logic are fused, semantic understanding, dynamic evaluation and adaptive optimization capabilities of a decision system are enhanced, and scientificity and real-time performance of enterprise decision are improved.

The invention discloses an intelligent management system and method for knowledge graph quality evaluation and self-repairing, belongs to the technical field of knowledge graphs, and aims to solve the problems that in traditional knowledge graph management, manual auditing efficiency is low, an effective automatic repairing means is lacked, and data complexity and real-time changes are difficult to deal with. The system firstly collects multi-source heterogeneous data in a target field, cleans the data through a deep learning noise recognition model, extracts entities and relationships by using a natural language processing technology, and adds metadata to convert the entities and relationships into graph structure data; then, a graph framework is defined based on the ontology, entity semantic alignment is achieved in combination with a graph neural network, and a knowledge graph is constructed by complementing implicit relations with the help of a pre-training language model. Then, the quality of the atlas is quantitatively evaluated through a four-layer quality evaluation system, meanwhile, a repair scheme is generated based on vulnerability feature extraction, knowledge base matching and decision fusion, and intelligent self-repair is achieved; the map can be monitored in real time and evaluated regularly, a repair strategy and a knowledge base are optimized through reinforcement learning, it is ensured that the map is kept accurate and time-efficient for a long time, and the practical value is improved.

The invention provides a gas turbine exhaust temperature sensor fault diagnosis system and method, and relates to the technical field of industrial equipment state monitoring and diagnosis. The system comprises a sensor signal acquisition module for acquiring an original temperature signal; the signal preprocessing module is used for acquiring and preprocessing original signals and working condition parameters; the feature extraction and windowing module is used for acquiring data and calculating features; the data driving diagnosis module is used for receiving the time sequence characteristics and evaluating the health state; and the decision fusion module is used for comprehensively analyzing the multi-source information and making a final fault diagnosis judgment. According to the system, the accuracy and the reliability of fault diagnosis can be remarkably improved, the false alarm rate and the missing report rate are reduced, early warning and accurate identification of early weak faults of the sensor are realized, the adaptability and the robustness of the diagnosis system to variable working conditions of the gas turbine are improved, and the safety, the economical efficiency and the operation and maintenance efficiency of operation of the gas turbine are improved.

The invention discloses a method for judging an RTK abnormal value in an automatic driving integrated navigation system, and relates to the technical field of automatic driving high-precision integrated navigation, and the method comprises the steps: collecting RTK observation data, inertial measurement unit data, a wheel speed pulse signal and LiDAR point cloud data, carrying out timestamp alignment and coordinate system unification, and generating a fusion data value; calculating a carrier-to-noise ratio weight signal quality index of the satellite through the fused data value, and generating a signal quality report; and combining the signal quality report, the current satellite geometric accuracy factor and the vehicle motion acceleration, calculating a residual threshold, constructing a coriolis force compensated double-integral prediction model by using inertial measurement unit data, and predicting the position of the vehicle at the current moment. According to the method, multi-dimensional features such as satellite signal quality, vehicle motion state and residual analysis are fused through multi-source decision, and the anomaly detection capability of complex scenes such as urban canyons is effectively improved.

The invention relates to the technical field of new energy storage regulation and control, and discloses an electricity-hydrogen coupling energy storage system and method for a new energy field station. The system comprises a feature analysis unit, a topology construction unit, a regulation and control domain positioning unit and a decision optimization unit. The feature analysis unit receives operation condition information of the new energy station, and generates a condition feature vector according to real-time load fluctuation, a historical output curve and an equipment operation database by using a deep feature extraction network; the topology construction unit constructs an energy topological graph for distributed energy storage equipment and hydrogen production equipment in the electricity-hydrogen coupling system, dynamically updates a graph structure and a node state based on a real-time monitoring mechanism, and deploys the graph structure and the node state in a heterogeneous computing framework; a regulation and control domain positioning unit positions a matched regulation and control domain in a dynamic representation learning mode according to the working condition feature vector; and the decision optimization unit utilizes a multi-source decision fusion algorithm to screen regulation and control instructions in a regulation and control domain and generate a scheduling sequence, so that the adaptability and the operation efficiency of the system to working conditions are improved.

The invention discloses a pipeline leakage detection device and method based on multi-modal data fusion, and relates to the technical field of pipeline safety monitoring. According to the device, modular sensor cabins (ultrasonic waves, LiDAR, millimeter wave radar and optics) are adopted for cooperative work, and high-precision leakage detection and positioning are achieved in combination with space-time registration, a cross-modal feature alignment network and a multi-level decision fusion mechanism. Through a dynamic weight distribution model and a multi-physical field joint inversion algorithm, the problems of low detection precision and poor anti-interference capability of a traditional single sensor are effectively solved. The method is suitable for detecting defects such as corrosion and leakage of metal and nonmetal pipelines, and has the advantages of high sensitivity, strong robustness and wide applicability.

The invention relates to the technical field of learning optimization, in particular to a big data analysis-oriented deep reinforcement learning optimization algorithm, which comprises the steps of dynamic feature topological graph generation: performing time sequence correlation analysis on a big data stream input in real time to generate a dynamic feature topological graph; an enhanced state space is reconstructed, wherein the enhanced state space with space-time correlation characteristics is reconstructed through potential path mining; hierarchical decoupling training: asynchronous parameter updating is carried out by adopting a gradient isolation mechanism, and a strategy gradient flow and a value gradient flow are generated; multi-dimensional decision fusion: generating a multi-objective optimization decision through dynamic weight fusion; and feedback driving adjustment: according to the actual execution effect of the multi-objective optimization decision, reversely adjusting the construction threshold of the dynamic feature topological graph and the dimension parameter of the enhanced state space. According to the method, by constructing the dynamic feature topological graph, the time sequence dependency relationship in the big data flow can be captured in real time, the dynamic evolution process between the features is effectively reflected, and the flexibility and precision of feature modeling are remarkably improved.

The invention discloses a method for estimating the safety communication rate of a near-earth satellite by using an SDFS cooperating with a GLRT. The method comprises the following steps: constructing a communication system model between the near-earth satellite and a ground terminal; analyzing the signal and channel characteristics, and obtaining the distance between the nodes, the channel gain and the received signal distribution; under the condition that the transmitting power of each monitoring party is unknown, estimating the transmitting power by adopting generalized likelihood ratio test, and independently executing the generalized likelihood ratio test based on the observation data of each monitoring party; the superior manager performs joint judgment based on a soft decision fusion scheme in combination with the generalized likelihood ratio test result of each monitoring party and a joint threshold so as to estimate the detection error probability of the monitoring party; a joint optimization strategy of the transmitting power and the number of continuous transmission time slots is constructed, and the total throughput of the system is maximized under the condition that the cooperative concealment constraint is met; according to the invention, an innovative solution is provided for reliable communication of the near-earth satellite in a complex monitoring environment.

The invention relates to the field of forestry monitoring data processing, in particular to a forestry pest and disease damage intelligent monitoring system and method based on unmanned aerial vehicle inspection, and the system comprises a data interaction unit which is used for building a real-time communication channel between an unmanned aerial vehicle and a server through employing a WebSocket protocol; the multi-scale feature extraction unit is used for constructing a three-stage pyramid convolution structure, and respectively extracting microscopic, medium and macroscopic scale features of the blade through convolution kernels of different specifications and cavity convolution; the time sequence parameter adjusting unit is used for fusing Transform and GRU, analyzing a feature map through a multi-head self-attention mechanism, learning a tree phenological law through the GRU, performing condition normalization adjustment on convolutional layer parameters based on the tree phenological law, and distinguishing physiological and disease features; and the decision fusion unit is used for splicing and fusing the composite feature map and the time sequence feature vector, carrying out parallel processing on an SVM and a Softmax classifier, and outputting a disease and pest recognition conclusion according to a dynamic threshold decision.

The invention discloses a projectile drop point detection method, system and equipment based on dual-light decision fusion, and a medium. The method comprises the following steps: collecting visible light and thermal infrared imaging data; preprocessing the data to obtain visible light and thermal infrared images of corresponding target region time-space registration; projecting a ground target area on the obtained image after space-time registration to a corresponding reference projection target surface; respectively inputting the registered images into a typical target detection model for target detection, judging whether an explosion phenomenon is detected in the images frame by frame, obtaining positioning information and category information in respective modes, dividing detection results into two categories of matched targets and unmatched targets, and outputting an explosion bounding box after secondary decision making; calculating and correcting the position of the drop point according to the explosion bounding box, and determining the image coordinate of the drop point; and mapping the coordinate of the drop point image to the reference projection target surface to obtain the position coordinate information of the drop point in the reference projection target surface. The method improves the detection precision, and better completes the target scoring task.

The invention relates to the technical field of hydraulic valves, and discloses a digital hydraulic valve intelligent diagnosis system which comprises a data acquisition module, a data preprocessing and label generation module, a feature fusion module, a classification diagnosis and decision fusion module and a service life prediction and suggestion generation module. Data such as pressure, vibration, displacement, flow and pollution degree of the valve are comprehensively captured through deployment of a multi-source sensor array at key positions of the digital hydraulic valve and a self-adaptive acquisition strategy, early fault feature omission is avoided, then through preprocessing means such as soft-hard hybrid wavelet threshold denoising and multi-sensor time alignment, the data precision is effectively improved, and the accuracy of the data is improved. Then, through dynamic-static layered feature fusion and an attention weighting mechanism based on GRU, different feature advantages under steady-state and fault working conditions are fully combined, the fault feature distinction degree is greatly enhanced, and the problem that similar faults are likely to be confused is solved.

The invention discloses an empty box recognition method, device and equipment and a storage medium, and relates to the technical field of logistics and cargo detection, the empty box recognition method comprises the steps that a real-time video stream of a to-be-recognized vehicle is acquired, and the vehicle comprises a box body capable of loading cargos; scanning the box body by using a laser radar to obtain three-dimensional point cloud data of the box body; on the basis of a pre-trained empty box recognition model, whether the box body is an empty box or not is determined according to the real-time video stream and the three-dimensional point cloud data of the box body, and the pre-trained visual empty box recognition model is obtained according to the images of the empty box body and the non-empty box body in the box door opening state and the three-dimensional point cloud data. And training the visual empty box identification model. According to the scheme, accurate recognition of the empty box is achieved through image and radar data association and decision fusion, the vehicle empty box recognition speed can be effectively increased, the workload of people is relieved, and the empty box recognition efficiency is improved.

The invention relates to the field of electric power monitoring, in particular to an electric power monitoring system intrusion detection method and system based on flow analysis. The method comprises the following steps: collecting network traffic, analyzing and recombining to obtain structured session data; time sequence behavior features and function code distribution features are extracted to construct a multi-dimensional feature set; inputting the feature set into a compliance rule base and a behavior baseline model in parallel, and respectively outputting a rule matching result and an abnormal deviation degree score; generating a comprehensive threat index by adopting a weighted decision fusion strategy; and when the index exceeds a dynamic threshold value, intrusion is determined and an alarm is given. According to the invention, the problem of insufficient precision and adaptability caused by single feature dimension and isolated detection mechanism is solved.

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.

A framework for decision fusion utilizing features extracted from vehicle images and their detected wheels. Siamese networks are exploited to extract key signatures from pairs of vehicle images. The present disclosure melds different types of similarity scores (whole vehicle and wheels / hubcaps) between target and test vehicles to robustly integrate different similarity scores and provide a more informed decision for vehicle matching. The present disclosure provides improved accuracy for side-view vehicle matching even under different illumination conditions and elevation angles.

The invention discloses a multi-modal data fusion and fault diagnosis method, and belongs to the field of transformer partial discharge fault diagnosis. According to the method, for the problems of false alarm and missing alarm caused by data isolation and lack of effective integration in partial discharge diagnosis of the transformer, acoustic, infrared and visible light multi-mode data are synchronously collected, pixel-level space alignment is carried out based on feature point matching, time sequence synchronization is achieved through hardware trigger signals, and the fault diagnosis accuracy is improved. Multi-level fusion diagnosis of a data layer, a feature layer and a decision-making layer is adopted, including channel superposition to form a fusion diagnosis image, voiceprint features, temperature rise features and arc light or corona features are extracted and input into a feature fusion model to obtain an associated feature vector, decision fusion is performed through a support vector machine classifier and a D-S evidence theory, and a decision-making result is obtained. And outputting a final diagnosis conclusion, thereby realizing accurate and reliable diagnosis of the partial discharge fault of the transformer.

The invention discloses a partition-based mesh power transmission and distribution network optimization system and method. The system comprises seven units of topological structure sensing and partition division, power flow data acquisition and feature extraction and the like. The topological structure sensing and partition dividing unit is used for dynamically dividing sub-regions according to a power grid node connection relation and load characteristics; the power flow data acquisition and feature extraction unit deeply extracts data features such as voltage and current, the improved depth Q network strategy generation unit combines power grid parameter construction state and action space to generate a preliminary strategy, and the optimized attention mechanism weight distribution unit distributes weights according to node electrical distance and line transmission capacity to perform strategy fusion. The multi-region collaborative decision fusion unit synthesizes sub-region power interaction and stability indexes to perform global decision, the control instruction generation and issuing unit issues instructions in combination with equipment constraint, and the operation state feedback and updating unit realizes state real-time updating, so that the power grid operation optimization efficiency and safety are improved.

The invention provides a gas detection system and device, and relates to the technical field of gas detection, and the system comprises a configuration module which is used for carrying out the type selection configuration of a monitoring sensor according to the monitored gas; the reference construction module is used for constructing reference grid density based on the minimum coverage area of the monitoring sensor; the monitoring node establishment module is used for performing region importance calculation on the to-be-monitored region and configuring monitoring nodes; the collaborative analysis module is used for acquiring and establishing a monitoring sensor array and establishing an associated collaborative coefficient; and the detection module is used for calling a decision fusion network to carry out abnormal decision making on the monitoring data based on the association cooperation coefficient, and generating a gas detection result. By means of the gas detection method and device, the technical problem that in the prior art, due to the fact that selection and configuration of the sensors are not adjusted according to actual requirements, the gas detection precision is low can be solved, the sensors are reasonably configured according to the gas type and the region importance, and the gas detection precision is improved.

The invention relates to the technical field of grinding machine on-line detection, and discloses a grinding machine working process on-line monitoring system, which comprises a data acquisition module for acquiring vibration, acoustic emission and environment signals and generating an original multi-dimensional signal vector; the data preprocessing module is used for preprocessing the original multi-dimensional signal vector to generate a purified signal set; the working condition inversion module is used for performing real-time inversion on a technological parameter estimation value of current grinding machining; the anomaly detection module is used for calculating and quantifying an anomaly score of the anomaly degree of the current working condition based on the purification signal set; the adaptive adjustment module is used for generating a dynamic threshold value according to the process parameters and comparing an abnormal score to judge an abnormal state; and the decision fusion module fuses the abnormal state and the environment humidity and generates a decision instruction. According to the method, the material hardness of the machined workpiece is estimated in real time, and the abnormal threshold value is dynamically adjusted according to the hardness estimation value, so that normal signal fluctuation and equipment faults caused by switching of normal working conditions are effectively distinguished, and false alarms caused by process changes are avoided.

The invention relates to the technical field of voice emotion recognition, in particular to a man-machine interaction voice sensing method and system based on a gradient intelligent calling subnet pool. The method comprises the steps of obtaining an emotion data set; constructing a man-machine interaction voice perception model based on a gradient intelligent dispatching sub-network pool; the system comprises an acoustic clue sensing purification module, a layered acoustic essential coding module, a gradient harmony subnet pool module, a task specific feature extraction module, a focus and confidence joint calibration module, a self-adaptive optimization strategy module and a real-time reasoning and decision fusion module. Carrying out emotion decision making by utilizing the constructed human-computer interaction voice perception model; and outputting a decision result. According to the invention, through the acoustic clue sensing purification module and the layered acoustic essential coding, the problems of emotional information distortion and identity feature confusion caused by real environmental noise are fundamentally solved.

The invention relates to the field of network public opinions, and discloses a public opinion information detection method based on a heterogeneous large model, which comprises the following steps: acquiring a public opinion text, performing sentence segmentation, denoising and word segmentation preprocessing, and inputting the processed text into a detection model to output a harmful information category and an early warning level. The detection model is composed of a first large language model and a second large language model, and has the structural characteristics of cross-architecture semantic alignment, hierarchical knowledge distillation, field attention enhancement, multi-channel decision fusion and the like. Wherein the cross-architecture semantic alignment realizes hidden space sharing through bidirectional projection; the hierarchical knowledge distillation dynamically distributes weights according to task contribution of each layer; introducing domain bias to enhance semantic focusing by domain-enhanced attention; and the output of the two models is adaptively integrated and classified through multi-channel decision fusion. According to the method, the accuracy, robustness and reasoning efficiency of public opinion harmful information detection are effectively improved.

The invention relates to a station building safety monitoring method based on multi-model decision and edge calculation optimization, and the method comprises the steps: obtaining station building image data, carrying out the processing of the data through employing a customized image enhancement technology, and constructing a sample set; designing a plurality of deep neural network models, performing mixed precision quantitative perception training on the models by using the sample set, and deploying the models at edge equipment; performing preliminary safety state detection on the power distribution room image which is acquired and enhanced in real time, and integrating preliminary detection results through a multi-model decision fusion mechanism; a cloud edge collaborative self-learning closed loop is established, conflicting, low-confidence or false detection samples of an edge end are transmitted back, a large model is used for auxiliary labeling and incremental training, a new model is issued after performance verification, and continuous iterative optimization is realized. According to the method, image enhancement, multi-model cooperation, edge calculation and an online learning mechanism are fused, the detection accuracy of the potential safety hazard of the station building in a complex environment and the self-adaptive capability of the system are improved, and a reliable technical scheme is provided for intelligent operation and maintenance of the station building.

The invention belongs to the technical field of hydroelectric generating set fault diagnosis, and particularly discloses a hydroelectric generating set intelligent fault diagnosis method based on multi-sensor data fusion. The method comprises the following steps: firstly, constructing two branch convolutional neural networks to respectively extract time domain features and frequency domain features of sensing data, and then constructing a central convolutional neural network to perform feature extraction on fusion features obtained by fusing the shallow time domain features and the frequency domain features; fusing the fusion feature obtained by the convolution of the # imgabs0 # layer and the time domain feature and the frequency domain feature obtained by the convolution of the # imgabs1 # layer in the branch convolutional neural network, and updating the fusion feature obtained by the convolution of the # imgabs2 # layer; outputting a preliminary diagnosis result by the central convolutional neural network; and finally, performing decision fusion based on information entropy on a preliminary diagnosis result obtained by the multiple paths of sensing data to obtain a final diagnosis result. Compared with the existing diagnosis method, the diagnosis method provided by the invention has higher anti-interference capability and robustness.

The invention relates to the technical field of fault section identification, and particularly discloses a fault section identification method and system for a multi-branch hybrid power transmission line system, and the method comprises the steps: collecting multi-modal parameters, carrying out the dynamic topology modeling, outputting an enhanced feature matrix and a dynamic impedance thermodynamic diagram of a multi-branch hybrid power transmission line, performing spatial-temporal feature decoupling and credibility evaluation to obtain a decoupling feature vector group and a feature credibility label, performing intelligent path traceability and conflict resolution on the multi-branch hybrid power transmission line, constructing multi-source decision fusion and anti-interference verification, labeling coordinates of a core fault section and verifying a credibility certificate, and performing knowledge distillation and closed-loop self-learning. A typical fault fingerprint database is established, and whether model reconstruction is triggered or not is judged. According to the method, the problems of long consumed time, high cost, low efficiency and prolonged power failure time of traditional power transmission line fault section identification are solved, the fault positioning time can be effectively shortened, and the difficulty of troubleshooting is reduced.

The invention discloses a concrete vibration sufficiency judgment method and system based on multi-sensor fusion. The method comprises the steps that first information of a vibration rod in concrete and second information in the concrete are obtained; the first information is motion state information; the second information is hydration reaction state information and comprises temperature change in the concrete; fusing the first information and the second information to obtain a fusion result; vibration sufficiency judgment is conducted according to the fusion result, and a vibration sufficiency judgment result is obtained; according to vibration sufficiency judgment, weighted decision fusion is carried out on the first information and the second information according to a preset weight, and judgment is carried out in combination with a self-adaptive threshold value and a state machine model; and generating operation guidance information based on the global region or the local sub-region according to the vibration sufficiency judgment result. According to the method, intelligent and accurate judgment on the concrete vibration sufficiency is achieved, the vibration effect can be evaluated more comprehensively and objectively, under-vibration or over-vibration is effectively avoided, and the concrete engineering quality is improved.

The invention provides a multi-modal computer vision data fusion method, and relates to the field of computer vision data fusion. The method comprises the following steps: 1, firstly, carrying out data alignment, obtaining a new image representation through pixel-level fusion, and then carrying out sensor fusion to integrate data into a uniform format for subsequent analysis; 2, feature fusion is carried out, firstly, feature splicing is carried out to serve as input of a model, then an attention mechanism is used to pay attention to more important modal information, and finally joint embedding is carried out to compare and analyze data; and step 3, finally, decision fusion is carried out, classification results are weighted through a voting mechanism, and then weighted averaging is carried out on data to obtain a final result. By processing heterogeneity, missing data and noise among different modals, data fusion is performed among different modals in advance, the fusion effect is optimized through a more efficient algorithm by secondary data processing, and the fusion efficiency is improved.

The invention provides a target positioning and detection method based on cooperative work of multiple unmanned aerial vehicles, and relates to the technical field of target identification and positioning. An unmanned aerial vehicle cluster is provided with a plurality of cameras, target images are collected in real time from different visual angles, a deep convolutional neural network is adopted to perform feature extraction on the images collected by each camera, and a cross attention mechanism is introduced to perform adaptive fusion on feature maps of different visual angles. The method comprises the steps of performing multi-scale target detection and target positioning in combination with a feature pyramid network, performing decision fusion by using a consensus algorithm in combination with positioning and detection results from different unmanned aerial vehicles in a decision fusion module, performing post-processing on a fused target recognition result, and outputting a final recognition result. The method not only improves the accuracy of target identification and positioning, but also provides effective technical support for the application of the unmanned aerial vehicle in the fields of military reconnaissance, outdoor search and rescue and the like, and has wide market prospects and application values.