125results about How to "Improve fusion efficiency" patented technology

The invention discloses an ecological product carbon sink value accounting method, belongs to the technical field of resource evaluation, and solves the problem that a regional carbon sink estimation result has deviation due to the fact that the existing method is insufficient in single remote sensing data coverage range and resolution and is difficult to adapt to spatial heterogeneity of a region to which a product belongs. The method comprises the following steps: acquiring multi-source heterogeneous carbon sink monitoring data associated with a sub-monitoring area in real time according to a preset acquisition period, preprocessing the multi-source heterogeneous carbon sink monitoring data, and performing data enhancement on a normalized data set based on a Mosaic-Mixup mixing method; executing the carbon sink value accounting model, wherein the carbon sink value accounting model outputs a carbon sink value accounting result; according to the method, a method of preprocessing the multi-source heterogeneous carbon sink monitoring data and performing data enhancement on the normalized data set is adopted, so that accurate and scientific data support can be provided for the carbon sink value accounting of the ecological product, and the carbon sink capacity of the regional ecological product can be accurately evaluated by the carbon sink value accounting model.

The invention discloses a transfer learning-based cross-data center storage and calculation cooperative training acceleration method. The method comprises the following steps of S1, deploying an initial version of a deep learning model in a plurality of heterogeneous data centers; s2, performing initial training on the deep learning model in the first data center to generate a first model parameter set; s3, generating a local deep learning model parameter set; s4, establishing a dynamic weight adjustment mechanism of the heterogeneous data center; s5, distributing different computing resources to each heterogeneous data center according to the participation weight; s6, scheduling storage resources and computing resources in real time according to resource use conditions and task requirements of the heterogeneous data centers; s7, performing multi-task parallel processing; s8, monitoring the resource utilization rate of each heterogeneous data center through a dynamic resource scheduling strategy; and S9, generating a final global optimization model parameter set, and applying the global optimization model parameter set to subsequent task deployment of each heterogeneous data center. According to the method, the collaborative training efficiency of the cross-heterogeneous data center is optimized.

The invention discloses a multi-sensor fusion vehicle rapid positioning method and device, a storage medium and electronic equipment, and belongs to the technical field of vehicle positioning. The positioning method comprises the following steps: firstly, acquiring standard data of a vehicle in a driving process on a preset planned path through multiple sensors, and constructing an offline three-dimensional map containing a key frame image database; secondly, collecting fact pose data in a vehicle driving process as initial pose estimation, collecting online current frame environment image data in real time through a camera, extracting a global feature vector in the online current frame environment image, matching K pictures similar to the image from a key frame image database, and carrying out initial pose estimation on the K pictures; and extracting local feature points and descriptors in the K pictures, screening out the most approximate picture from the K pictures as a target picture, and calculating to obtain real-time pose estimation of the vehicle. According to the method provided by the invention, more efficient, more accurate and more reliable vehicle positioning is realized through the cooperation of online processing and offline processing.

The invention discloses a data fusion method, apparatus and device, and a storage medium. The method comprises the steps of obtaining scale data in each database; by means of a preset semantic analysis model, scale fields in the scale data are transferred into standard names in the CDISC standard, and a field mapping relation and a field value mapping relation are constructed; converting the scale data into target data conforming to the CDISC standard based on the field mapping relation and the field value mapping relation; and performing data fusion on the target data according to the weight of each database by adopting a weighted average method to obtain a data fusion result. The scale field in the scale data is transferred into the standard name by utilizing the preset semantic analysis model, the scale data is converted into the target data meeting the CDISC standard on the basis of the field mapping relation and the field value mapping relation, and finally, the target data is fused by adopting the weighted average method, so that the data fusion efficiency is improved. And the efficiency and reusability of cross-column research data fusion are improved.

The invention discloses a multi-exposure image fusion method based on a depth separable Transform and a CNN cascade network. According to the method, a weight sequence obtained through channel self-attention calculation is used for making up for the potential loss of a synergistic effect between channels. Wherein the learnable weighted summation process for obtaining the weight sequence is beneficial to strengthening the interaction between input feature channels, and provides guidance information for adaptively adjusting the information importance degree of different channels. In the feature extraction module, the model can effectively reuse features and reduce the depth and parameter quantity of the network through the form of a dense connection network and the design of a deep coupled convolution block and a Transform block. According to the method, the calculation amount is greatly reduced, and meanwhile, the weight sequence obtained by channel self-attention calculation is utilized to make up for the potential loss of the synergistic effect between channels, so that the model can effectively reuse the features, and the network depth and the parameter amount are reduced. The method provided by the invention is superior to an existing advanced algorithm in visual performance and fusion speed, and an efficient and effective solution is provided for the field of multi-exposure image fusion.

The invention relates to the technical field of extruders, and provides an anti-solidification double-screw extruder for cable material production, which comprises a shell and a feeding hopper, and screw rods are movably installed in the shell side by side, a feeding hopper is fixedly installed on one side of the top end of the shell, a stirring mechanism is movably arranged in the feeding hopper, and the stirring mechanism comprises a second belt pulley mechanism, a connecting shaft, a stirring shaft, a first right-angle gear, a second right-angle gear and a groove. By arranging a stirring mechanism, when a screw rotates for processing, a second belt pulley mechanism is matched with a right-angle gear, so that a stirring shaft rotates, a stirring plate is driven to stir and mix cable raw materials, grinding wheels on the two sides rotate and grind in a groove, and the cable raw materials are ground; therefore, in the subsequent processing and extruding process of the double-screw extruder for cable material production, raw materials are uniformly distributed, the fusion efficiency and the extrusion speed are increased, the extrusion difficulty is reduced, and faults such as screw locking are avoided.

The invention discloses a gas hydrate cold storage device and method, and belongs to the field of cold storage and energy conservation. A cover is arranged on the hydrate generation kettle, an exhaust port and an air inlet are formed in the cover, and a liquid outlet and a liquid inlet are further formed in the side face of the hydrate generation kettle; the output end of the air storage tank is sequentially connected with an air pump, a one-way valve I, a control valve, a compressor and an air inlet; the carbon dioxide generating tank is connected with the input end I of the gas storage tank, and a one-way valve II is arranged between the carbon dioxide generating tank and the input end I of the gas storage tank; a one-way valve IV is arranged between the separator and the gas storage tank; and a heat exchange pipe is arranged on the outer side of the hydrate generation kettle. According to the device, the fusion efficiency of gas and water can be improved, gas which cannot be fused with water can be introduced into the cylinder again, and after the gas is mixed with gas in an original pipeline, the gas in the original pipeline is cooled in advance and then enters a hydrate reaction kettle to rapidly reach the preset temperature, and the fusion efficiency is further guaranteed.

The invention discloses a target detection method and system fusing DOM and DSM data, and belongs to the technical field of target detection, and the method comprises the steps: obtaining original image data, carrying out the preprocessing, and making DOM data of a target region; constructing a digital surface model based on the original image data to obtain DSM data of the target area; performing feature fusion on the DOM data and the DSM data through a graph fusion model to obtain multi-modal fusion data; and making a training set based on the multi-modal fusion data, training a target detection model, and performing building identification through the target detection model. According to the invention, the DOM data and the DSM data of the target area are constructed based on the original image data, and feature fusion is carried out through the graph fusion model to obtain the multi-modal fusion data to carry out building target detection training, so that information complementation is carried out on the data of different modals, the information amount of the data is enriched, and the accuracy of building target identification is improved.

The invention discloses a system for identifying a writing style based on a handwritten information style characterization feature, and the system comprises a collection module which collects a handwritten information handwriting time sequence data sequence, a preprocessing module which normalizes handwriting point position sequence features, sets a fixed point position, carries out the resampling, and inputs a backbone network; the backbone network acquires time sequence features and global matching features therein, and the feature fusion network performs style information fusion on the time sequence features of the time sequence level and the global matching features of the global matching level to obtain an overall style representation vector of the handwritten information handwriting; and the supervision module calculates the overall style similarity of the two samples according to the overall style representation vectors of the different handwritten signature samples, and judges whether the handwritten information is handwritten information of the same writing style or not. Time sequence and matching features are obtained at the same time through a graph network, a matching information model is supervised by the network, multi-task training is performed on a representation model, and an overall level, a time sequence level and a global matching level are supervised at the same time. And accurate judgment on the handwriting style is obtained.

The invention discloses a multi-dimensional remote sensing data fusion method based on multi-task learning, and relates to the field of remote sensing technology. The multi-dimensional remote sensing data fusion method based on multi-task learning comprises the following steps: Step 101, based on a time series high spatial resolution data set, a time series high temporal resolution data set and a time series high spectral resolution data set to be fused, the data are sorted and stored according to the time dimension, the spatial dimension X, the spatial dimension Y and the spectral dimension. Compared with the prior art, the beneficial effects of the invention are as follows: the invention constructs a spatial information extraction model, a temporal information extraction model and a spectral information extraction model by constructing four-dimensional high spatial resolution data, four-dimensional high temporal resolution data and four-dimensional high spectral resolution data, and utilizes a multi-task learning multi-feature loss function design to realize the joint extraction and fusion of time, space and spectral dimension information, thereby realizing integrated fusion in multiple dimensions and improving the efficiency and speed of data fusion.

The invention provides a crop phenotype data fusion method and device, electronic equipment and a storage medium, and relates to the technical field of electrical digital data processing, and the method comprises the steps: inputting target data corresponding to each piece of three-dimensional first to-be-fused crop phenotype data in each piece of to-be-fused crop phenotype data into a weight adjustment model, and obtaining a weight value corresponding to each piece of first to-be-fused crop phenotype data output by the weight adjustment model, and performing data fusion on each piece of to-be-fused crop phenotype data based on the weight value corresponding to each piece of first to-be-fused crop phenotype data and internal and external parameter data of a crop phenotype sensor for collecting each piece of to-be-fused crop phenotype data, the target data comprises environmental data when the crop phenotype data is collected, relative position information between the crop phenotype sensor and the crop, and a target parameter value of the crop phenotype data. According to the method, the dynamic weight is given to the crop phenotype data to be fused, so that the fusion effect, relevance, fusion efficiency and timeliness of data fusion can be improved.

The invention discloses an environment-friendly sewage treatment device for a textile printing and dyeing workshop, and relates to the technical field of sewage treatment.The environment-friendly sewage treatment device comprises a shell, a top cover is fixedly installed at the top of the shell, a water inlet pipe penetrates through the top of the shell and is fixedly installed on the top of the shell, a sewage draining exit is fixedly installed on the right side of the shell, and a rotating motor is arranged at the top of the top cover; a stirring rod is fixedly installed at the output end of the rotating motor, the stirring rod penetrates through and is rotatably installed on the inner wall and the outer wall of the top cover, the filter tank is fixedly installed on the inner wall of the shell, the filter screen is fixedly installed on the inner wall of the filter tank, the linear motor is arranged on the surface of the shell, and the L-shaped rod is fixedly installed on the surface of the linear motor. The linear motor moves towards the sewage draining exit to drive the L-shaped rod to move towards the sewage draining exit, so that the filtered impurities can be effectively cleaned, the situation that the sewage draining effect becomes poor due to excessive accumulation of the impurities is prevented, and the working efficiency is improved.

The utility model belongs to the technical field of medical instruments, and particularly relates to a 3D printing expandable interbody fusion cage which comprises a fusion cage body, and the fusion cage body comprises a screw, a fixing pin, a transverse pin, a frame body, an upper base plate, a lower base plate and a supporting sliding block arranged between the upper base plate and the lower base plate. The distractable interbody fusion cage and intervertebral contact parts are manufactured by using a 3D printing technology, and three-dimensional porous structures similar to cancellous bone structures are designed on the surfaces of the upper base plate and the lower base plate, so that the fusion rate of the interbody fusion cage and the spine of a human body can be increased; meanwhile, the interbody fusion cage is simple in structure and convenient to operate, is implanted into the interbody space of the human body in a contracted state, is expanded in situ, can increase the contact area with an end plate and the bone chip filling space, can recover the forward convex curvature, has high biomechanical stability, is suitable for various lumbar degenerative diseases needing interbody fusion, and can be widely applied to the field of lumbar interbody fusion. The clinical treatment effect can be effectively improved.

The invention discloses a multi-modal large model training strategy determination method and system, and particularly relates to the technical field of multi-modal data analysis. Comprising a training task complexity module, a task matching model determination module, a model training multi-modal data acquisition module, a model training performance monitoring module, a model training strategy early warning processing module, a model training multi-modal fusion effect analysis module and a model training multi-modal fusion effect evaluation module. According to the method, the optimal execution large model of each training task is calculated, the multi-modal data is collected, the performance stability index is obtained through calculation, and the modal fusion effect evaluation coefficient is obtained through analysis, so that a quantitative index is provided for evaluating the training task of the large model, the overall training efficiency is improved, and the task execution risk early warning index is calculated; real-time task training risk early warning measures are provided, the rationality of evaluation of the execution condition of the training tasks is improved, and automatic early warning management of large-model training task abnormities is achieved.

The invention belongs to the technical field of radar data processing, and particularly relates to a multi-radar-based target detection data fusion method and system, and the method comprises the steps: obtaining N initial tracks of at least one target through N radars, introducing the trace point density and the density sensitive distance according to the initial tracks, and carrying out the clustering of the initial tracks, therefore, a unique and reliable target movement track is generated; according to the method, the similarity and the difference between the target trace point and the track can be accurately evaluated under the condition that the calculation amount is small, the problem of inaccurate evacuation trace point fusion is effectively avoided, and therefore the target tracking precision and the robustness are improved.

The utility model relates to the field of lubricating oil synthesis, in particular to a reaction kettle for synthesizing lubricating oil. The technical problems that in the lubricating oil synthesis process, due to the fact that base oil and additives are prone to being attached to the inner walls of most reaction kettles, the reaction is incomplete due to uneven stirring, meanwhile, heat is generated in the lubricating oil synthesis process, the pressure in the reaction kettles is prone to being increased, the reaction kettles are prone to being damaged, and the reaction effect of the lubricating oil is affected are solved. And the reaction efficiency of the lubricating oil is reduced. According to the technical scheme, the synthetic reaction kettle for the lubricating oil comprises a reaction kettle, the device further comprises a cooling assembly, a stirring assembly, supporting legs, a fixing ring, a fixing bottom plate, a top cover and a movable sealing cover. The water pump of the water tank is started, and cooling water flows into the water condensing pipe through the water inlet pipe, so that heat generated in the synthesis process of lubricating oil is cooled, and the situation that a reaction kettle is damaged and sufficient reaction of base oil and additives is affected due to too high temperature is avoided.

The invention relates to a multi-modal medical image fusion method based on image denoising and illumination enhancement. The method comprises the following steps: inputting a multi-modal medical image into an illumination and contrast enhancement network for illumination intensity correction to generate an enhanced image; adding random noise to the multi-modal medical image to obtain a noise image; inputting the noise image into a denoising and reconstruction network to carry out denoising and reconstruction tasks; using the enhanced image and the multi-modal medical image as labels to jointly supervise and train a denoising and reconstruction network to obtain a feature encoder and a feature decoder with denoising and illumination enhancement functions, using the trained feature encoder to respectively extract features of different modal images to obtain modal feature maps, and using the modal feature maps to obtain the multi-modal medical image; and carrying out feature fusion on the modal feature maps of the different modal images, and reconstructing the fused feature maps by adopting a trained feature decoder to obtain a multi-modal medical image fusion result. The problems that image fusion is interfered by noise, illumination is degraded and fusion efficiency is low are solved.

The invention relates to the technical field of aviation, in particular to an aircraft control method based on L1 adaptive control, which comprises an aircraft, a two-dimensional laser radar and a depth camera are arranged on the aircraft, and the two-dimensional laser radar and the depth camera are in signal connection with a space synchronization sensor. The space synchronization sensor is in signal connection with a time synchronization sensor, the time synchronization sensor is electrically connected with a data preprocessing sensor, the data preprocessing sensor is in signal connection with an improved Bayesian new fusion algorithm sensor, and the improved Bayesian new fusion algorithm sensor is in communication connection with a fusion point cloud. The fusion point cloud is in communication connection with OctoMap, the autonomous navigation and obstacle avoidance capabilities of the aircraft can be improved by optimizing data quality and realizing deep fusion of multi-sensor data, and an efficient three-dimensional modeling mode also provides powerful data support for subsequent application of the aircraft.

The invention provides an image processing method, electronic equipment and a storage medium. The method comprises the following steps: determining a ground area of a virtual scene graph; acquiring a first camera parameter corresponding to the target position under a first visual angle; determining a first figure graph from a preset database based on the first camera parameter; according to a second camera parameter corresponding to the first figure graph, first pixel distribution of the first figure graph and a third camera parameter corresponding to the virtual scene graph under a third visual angle, a transformation matrix is transformed from the second visual angle to the third visual angle, and a second figure graph with second pixel distribution is obtained; based on the depth information of the second figure graph, adding the second figure graph to the target position to obtain a first composite graph; and obtaining a second composite image based on the first composite image and the image generation model. The method can improve the synthesis effect of the synthesized picture.

This invention relates to a method for fusion of heterogeneous knowledge in the power industry, comprising: obtaining first triples containing entity relations from the ontology layers of at least two knowledge graphs; classifying the entity relations in the first triples into synonym relations and inclusion relations according to relation aggregation prompts by a language model; merging entity relations belonging to synonym relations; obtaining second triples containing entity attributes or relation attributes from the ontology layers of at least two knowledge graphs; classifying the attributes in the second triples into synonym attributes and non-synonymous attributes according to attribute aggregation prompts by a language model; merging entity attributes or relation attributes belonging to synonym attributes; obtaining entities from the instance layers of at least two knowledge graphs and dividing them into several data blocks; determining whether there are duplicate entities according to entity alignment prompts by a language model; and merging duplicate entities.

The invention discloses a road segmentation method based on multi-modal feature adaptive progressive fusion, and the method comprises the following steps: constructing a road image segmentation data set which comprises a plurality of road RGB images obtained through photographing and a plurality of road depth images obtained through laser radar; constructing a road segmentation model, wherein the road segmentation model is composed of an RGB information coding module, a depth information coding module, a feature conversion module, a feature progressive adaptive fusion module and a multi-scale feature decoding module; inputting the road RGB image and the road depth image into a road segmentation model for fusion segmentation to obtain a final road segmentation result; according to the invention, through the feature conversion module and the feature progressive adaptive fusion module which are uniquely designed, the depth information can dynamically supplement RGB information, so that the precision and robustness of the road segmentation model in a complex scene are improved, and the road segmentation model especially has more excellent performance in low-illumination, shielding and dynamic environments.

The present application discloses a method for hybridizing daylily and Alstroemeria and its application in inducing Alstroemeria callus, belonging to the field of biotechnology. In the present application, the inventor uses asymmetric fusion somatic cell hybridization technology to fuse daylily and Alstroemeria protoplasts using PEG fusion method, and then culture to obtain embryonic callus. Compared with ordinary leaf induced callus, the induction success rate is higher. This method can not only solve the problem of low induction rate of Alstroemeria callus, but also improve the fusion efficiency of the two parents of daylily and Alstroemeria, and has made a breakthrough in the research of Alstroemeria callus induction and differentiation.

The invention provides an imaging method and system in a strong light environment based on multi-exposure fusion, and relates to the technical field of image processing, and the method comprises the steps: obtaining K RGB images of a target region, and converting the K RGB images from an RGB space to an HSV space, thereby obtaining K HSV images; extracting V channels of the K HSV images to obtain K V channel images; obtaining K weight graphs based on the K HSV images; constructing K weight Gaussian pyramids based on the K weight graphs, further constructing K V-channel Gaussian pyramids, and constructing K Laplacian pyramids based on the K V-channel Gaussian pyramids; obtaining a fused Laplacian pyramid based on the K weight Gaussian pyramids and the K Laplacian pyramids; constructing a fused image based on the fused Laplacian pyramid; and converting the fused image from the HSV space to the RGB space to obtain a final imaging result. According to the method, the calculation amount is reduced, and the fusion efficiency is greatly improved.

The application relates to the technical field of artificial intelligence, in particular to a model fusion method and device based on a dynamic topology, which comprises the following steps: based on an initial topology formed by a plurality of nodes participating in multi-task learning, sequentially performing model first fusion and model second fusion on all nodes in the initial topology, and determining the performance scores corresponding to the nodes; iteratively updating the initial topology according to the performance scores to realize dynamic topology construction until a preset update end condition is met, and a fusion model is constructed. Through the decentralized architecture and the dynamic optimization strategy, the efficiency and adaptability of model fusion are significantly improved, especially in the multi-task scene, the fusion order and node update are dynamically adjusted, the heterogeneity requirements of different tasks are effectively balanced, the generalization ability and robustness of the model are significantly improved, and the scale application of the multi-task learning technology in complex actual scenes is promoted.

The utility model relates to the technical field of organic fertilizer fermentation devices, in particular to a stirring assembly and an organic fertilizer ferment.The stirring assembly comprises a tank body, a top plate is fixedly connected to the upper end of the tank body, a feeding pipe is fixedly connected to one side of the top plate in an inserted mode, a fixed shaft rod is connected to one end of the top plate in an inserted mode, and a storage barrel is rotationally connected to the lower end of the top plate; a plurality of stirring pipes are fixedly inserted into the lower end of the storage barrel, a plurality of discharging holes are formed in one side of each stirring pipe, and baffles are fixedly connected to the two sides of each stirring pipe; the device has the beneficial effects that an additive is injected into the material storage barrel through the feeding pipe and then flows to the stirring pipe from the material storage barrel, when the material storage barrel and the stirring pipe are driven by the fixed shaft rod to rotate, the additive flowing into the stirring pipe can flow out of the multiple discharging holes in the stirring pipe, and the stirring pipe is obliquely arranged, so that the stirring efficiency is guaranteed, and meanwhile, the stirring effect is improved. An additive can be injected into each layer of the fertilizer, so that the fusion efficiency of the additive and the fertilizer is greatly improved.

The present invention discloses a method for calculating the value of carbon sink of an ecological product, which belongs to the technical field of resource assessment. The method solves the problem that the existing method has insufficient coverage and resolution of a single remote sensing data, is difficult to adapt to the spatial heterogeneity of the region to which the generated product belongs, and thus leads to deviations in the regional carbon sink estimation results. The method comprises: collecting multi-source heterogeneous carbon sink monitoring data associated with a sub-monitoring region in real time according to a preset collection period, preprocessing the multi-source heterogeneous carbon sink monitoring data, and performing data enhancement on a normalized data set based on a Mosaic‑Mixup hybrid method; executing a carbon sink value accounting model, and the carbon sink value accounting model outputs a carbon sink value accounting result; the present invention adopts a method for preprocessing the multi-source heterogeneous carbon sink monitoring data and performing data enhancement on the normalized data set to provide accurate and scientific data support for the carbon sink value accounting of ecological products, thereby ensuring that the carbon sink value accounting model accurately evaluates the carbon sink capacity of regional ecological products.

The invention discloses a high-temperature and high-pressure gas-containing turbine experimental device, comprising a dynamometer connected to a hydraulic turbine, a liquid storage tank, a liquid supply tank with an electric heating tube, a pressure-stabilizing tank, a gas storage tank, a hot water heat pump unit with a tube bundle heat exchanger, an expansion tube made of a magnetophilic metal, and an electromagnetic mixing device, wherein the pressure-stabilizing tank is placed on a vertically adjustable lift; the electromagnetic mixing device is composed of a magnetic wheel and a magnetic component matched with the magnetic wheel, and the magnetic component is externally connected to a power supply; the magnetic wheel is placed in the inner cavity of the expansion tube, and the magnetic component is sheathed on the periphery of the expansion tube. The device can be used to study the flow of gas-containing hydraulic turbines under high temperature and high pressure and the working mechanism of gas-containing hydraulic turbines.

The invention provides an image information acquisition method and device, electronic equipment and a chip, and relates to the technical field of digital image processing, in particular, the method comprises the following steps: firstly, carrying out superpixel segmentation on a to-be-processed pixel in a to-be-processed image to obtain superpixel information, the to-be-processed image comprising a reference frame image and a reference frame image, and based on a semi-global matching algorithm, dense motion vector information is generated according to the superpixel information, and the dense motion vector information is used for image fusion of the reference frame image and the reference frame image. According to the technical scheme, after the superpixel information is obtained from the to-be-processed image, the dense motion vector information serving as the image fusion information is further generated according to the superpixel information by using the two-dimensional semi-global matching algorithm, so that the obtaining speed of the image fusion information can be effectively improved, the operation complexity and the operation power consumption are reduced, and the image fusion efficiency is improved. Therefore, the image fusion efficiency can be effectively improved, and the requirement for equipment hardware is reduced.

The invention relates to the technical field of artificial intelligence, and provides an operator fusion method and system, electronic equipment and a storage medium, and the method comprises the steps: receiving the operator number of to-be-fused operators and an equipment function identifier sent by a host, and the equipment function identifier is determined by the host based on the operator identifier of the to-be-fused operators; based on the device function identifier, loading a device function corresponding to the to-be-fused operator, the device function being developed in advance based on the to-be-fused operator, and being compiled and loaded into a memory of the device; and fusing the operators to be fused based on the number of the operators and the equipment function. According to the invention, automatic fusion during operator operation can be realized, the development amount of operator fusion is greatly reduced, and the efficiency of operator fusion is improved.

The invention discloses a spectrum sensing data fusion method and device, equipment and a medium. The method is applied to a cognitive radio network, and comprises the following steps: acquiring perception data of a plurality of perception users on a communication state of a master user through a network convergence center; based on a plurality of time slots divided by a sliding window, determining updated spectrum sensing data in a first time slot according to a consistency fusion rate and a sensing state, the sensing state being whether a sensing user performs cooperative sensing with an adjacent sensing user; determining an updated reputation value according to the initial reputation value and the perception state; determining the sensing users with the updated reputation values smaller than the initial reputation value as malicious users and removing the malicious users; and under the condition that the updated spectrum sensing data of the plurality of sensing users do not meet the consistency condition, returning to the step of determining the updated spectrum sensing data in the next time slot until the consistency condition is met, and obtaining a fusion result. According to the embodiment of the invention, the quality of fused data can be improved.