20results about How to "Improve task performance" patented technology

The invention provides an aerial remote sensing multi-platform collaborative observation task planning and optimization method and system, and relates to the technical field of collaborative planning, and the method comprises the steps: receiving the observation demand information of a target region and the resource information of a plurality of aerial remote sensing platforms, building a multi-platform collaborative observation task model based on a deep reinforcement learning algorithm, generating an initial task planning scheme by utilizing the constructed multi-platform collaborative observation task model, optimizing the initial task planning scheme by adopting an improved multi-target particle swarm optimization algorithm, and distributing corresponding specific execution instructions to each aerial remote sensing platform through a security encrypted communication link; state information, including positions, postures, energy levels and equipment working states, fed back by the aerial remote sensing platforms is received and processed in real time, and a current task planning scheme is dynamically adjusted according to the real-time state information based on a federated learning framework by utilizing edge nodes and cloud cooperative computing capability, so that adaptive optimization of tasks is realized.

The invention belongs to the field of airplane design, and particularly relates to a multifunctional rear sealing gate of a heavy conveyor, which comprises a cabin door, a sealing door and an inclined platform, the cabin door is located at the tail of the airplane, and the sealing doors are located on an inclined table on the inner side of the cabin door and comprise the upper sealing door and the lower sealing door. One end of the upper sealing door is hinged above the exit of the cargo hold through a first rotating shaft, and the other end of the upper sealing door is provided with a second rotating shaft; a control device is arranged on the second rotating shaft and used for connecting the upper sealing door and the lower sealing door and driving the lower sealing door to rotate around the second rotating shaft or unlocking the lower sealing door so that the lower sealing door can be separated from the upper sealing door. A control device is arranged on the third rotating shaft and used for connecting and locking the inclined table and the lower sealing door and driving the lower sealing door to rotate around the third rotating shaft or unlocking the lower sealing door to be separated from the inclined table.

The invention provides a satellite system wave position scheduling planning method for a specified area. The method comprises the following steps: step 1, inputting satellite system wave position scheduling planning related information; 2, dividing sub-regions according to input information; 3, calculating the maximum slope distance according to the input information; step 4, utilizing the maximum slope distance and the divided sub-regions to carry out wave position scheduling planning; and step 5, outputting an optimal wave position scheduling plan. According to the method, the designated area is divided into the union set of the limited number of disjoint sub-areas, the coverage range is measured through the number of the beam position coverage sub-areas, and the problems that the beam position coverage range is irregular and is difficult to effectively select are solved. Moreover, the method can solve the problem of wave position scheduling for the designated area information acquisition and transmission task, achieves the efficient scheduling of the satellite-borne antenna wave position, and improves the task efficiency.

The invention provides a high-precision attitude estimation method for processing limited data based on a pre-training diffusion model, and the method comprises the steps: carrying out the preprocessing of an input image, and calling a function to combine with noise to generate a key point prompt; mapping the input image through an encoder to obtain a potential code, and increasing Gaussian noise of different amplitudes on the potential code to obtain a noise image; performing attention mechanism processing on the noise image to obtain a semantic corresponding point between the input image and the reference image; training the image pair, integrating training results of the basic class image and the new class image, and inputting an integration result into an Adam optimizer for optimization to obtain an optimization result; evaluating the image pair; according to the method, the key points of the unlabeled image data can be learned in a cross-category mode, a good attitude estimation effect is achieved under the condition that the data is limited, and the actual application value of attitude estimation is improved.

The invention discloses a fruit fly larva tumbling motion modeling and optimizing method based on a material point method. Aiming at a collaborative design challenge of a larva rolling motion model and the problems that a traditional method depends on manual data measurement and is time-consuming in training, a neural mechanical model is designed in combination with biological characteristics of drosophila larvae, and an artificial neural network based on deep learning is constructed as a model controller for training; meanwhile, a hyper-parameter optimization method based on a TPE algorithm is designed. According to the method, the method is embedded into a microprogrammable framework, and high integration and efficiency optimization of the method are achieved. The method can effectively simulate and optimize the rollover motion behavior of the drosophila larva under limited computing resources. The soft body model with the biological characteristics of the drosophila larvae has the advantages of flexible movement and adaptability to complex environments. According to the method, the optimal control strategy in the rollover process of the drosophila larvae is explored, interaction between the drosophila larvae and a complex environment is simulated, and important reference is provided for design of a soft robot similar to the drosophila larvae in reality.

The invention relates to the technical field of knowledge graphs, in particular to a knowledge graph construction method and system based on unsupervised learning, and the method comprises the steps: obtaining text data, carrying out the preprocessing of the collected text data, and carrying out the word segmentation of the text data, and obtaining a word segmentation result of the text data; based on a pre-training language model, performing entity recognition on the word segmentation result of the text data, and performing ambiguity elimination on an entity recognition result in the text data; an unsupervised model is adopted to extract entity relationships, and entities and entity relationship triples are generated; constructing an entity relationship weight calculation formula, mapping entity relationship weights into entity relationship confidence coefficients, and filtering entity relationships with confidence coefficient values lower than a threshold value; and constructing a knowledge graph of the text data based on the entities extracted from the text data and the entity relationship. According to the method, the efficiency and quality of knowledge graph construction are improved, and important knowledge support is provided for knowledge-driven applications such as intelligent search, questions and answers, recommendation and the like.

The invention relates to a remote sensing satellite task planning application performance evaluation method. The method comprises the following steps: establishing a hierarchical evaluation index system; definition and quantification of task completion capability indexes are completed; defining and quantifying resource utilization efficiency indexes; defining and quantifying a resource collaboration efficiency index; performing quantitative evaluation on the relative importance degree of each index element in the same level in the hierarchical structure and constructing a judgment matrix; checking the consistency of the judgment matrix; according to the judgment matrix, the proportion of elements in the hierarchical structure in the decision is calculated, and parameter weights including hierarchical single sorting and hierarchical total sorting are determined; and calculating a comprehensive efficiency evaluation value according to each index numerical value and the parameter weight. According to the method, quantitative evaluation of the application efficiency of satellite task planning can be realized, and closed-loop optimization of a task planning function in a remote sensing satellite management and control system is supported.

A system (SYS) and related methods for predicting patient treatment options. The system may include an input interface (IN) for receiving input data including biodynamic measurements regarding a patient. A predictor module (PM) is configured to process the biodynamic measurements to obtain output data comprising an indication of a treatment option for the patient.

The invention belongs to the technical field of aircraft trajectory optimization, and discloses an aircraft reentry trajectory intelligent generation method based on an RBF neural network. According to the method, firstly, an aircraft reentry model is constructed, a trajectory optimization problem needing to be solved is constructed based on the aircraft reentry model, an optimal sample trajectory set is generated by using a pseudo-spectral method according to an initial discrete height speed, and then a sample trajectory is generated at each waypoint according to a new discrete state. The sample set is divided according to the waypoints, the RBF neural network is trained, the input of the RBF neural network is a discrete height speed state, and the output of the RBF neural network is a state-control sequence from the current state to a target point; and finally, the RBF neural network is used for online generation of an optimal track. According to simulation verification, the method is high in confidence coefficient and small in error, and the generation efficiency of the optimal track can be greatly improved.

The invention discloses an equipment cluster maintenance decision-making method based on multi-agent deep reinforcement learning, and aims to solve the problem of collaborative maintenance of equipment clusters deployed in a multi-site distributed manner under the condition of dynamic change of task requirements. According to the method, on the basis of equipment health status, performance level and task requirements, an equipment cluster maintenance decision process is modeled as a Markov decision process, a centralized training-distributed execution multi-agent deep reinforcement learning framework is adopted, a local strategy network is constructed for each geographical deployment site, and the maintenance decision process is optimized. And meanwhile, a centralized state value evaluation network is utilized to evaluate the overall operation state of the equipment cluster, so that collaborative optimization of equipment cluster maintenance decisions deployed across sites is realized. By training a multi-agent strategy in a simulation environment and performing online execution in actual operation, each agent can autonomously generate a maintenance strategy based on a local equipment state, and comprehensive optimal control of overall task capability and maintenance cost is realized on a system level. According to the method provided by the invention, the self-adaptive optimization of the equipment cluster maintenance strategy can be realized under the complex conditions of a large number of equipment, high state dimension and dynamic change of task requirements, so that the overall task performance of the equipment cluster is improved, and the maintenance cost is reduced.

A multi-mode autonomous underwater robot, which is a whole body streamlined structure of a rotary body, is composed of a bow load cabin section, a buoyancy adjusting cabin section, a battery cabin section, a navigation cabin section, a control cabin section, a steering cabin section, a propelling cabin section and an antenna in series by sequentially arranging, and is sealed and connected between adjacent cabin sections by a hoop. In the aspects of hardware and structure, the multi-mode autonomous underwater robot has the characteristics of modularity and easy expansion. When the function needs to be expanded, the load sensor in the bow load cabin section can be replaced as needed, all cabin sections adopt the same electrical interface scheme, and the function expansion can also be realized by additionally installing an additional sensor load cabin section. The multi-mode autonomous underwater robot realizes the function compatibility of an AUV, a floating buoy, an ARGO buoy and an underwater glider on the same underwater robot, can realize five operation modes of a water surface navigation mode, an underwater navigation mode, a floating buoy mode, an ARGO buoy mode and an underwater glider mode, the operation modes can be freely switched, and the application range and task capability of the underwater robot are greatly expanded.

The present application relates to a method and electronic equipment for simulating and training unmanned aerial vehicle control. The method comprises acquiring an image sequence set, wherein the image sequence set comprises a plurality of image sequence subsets, wherein the image sequence subsets comprise a plurality of frames of images, wherein the images comprise at least one preset target object, wherein the number of preset target objects contained in the plurality of frames of images of the same image sequence subset is equal, and the number of preset target objects contained in the images of different image sequence subsets is different; displaying the plurality of frames of images of the plurality of image sequence subsets in sequence according to a preset time interval and a preset order; acquiring the actual target object selected by a control player according to the image within each preset time interval; judging for each image whether the actual target object selected by the control player is consistent with the preset target object of the corresponding image, and obtaining the judgment result; and determining the training score of the control player according to the judgment result.

The application provides a task execution method and device based on agent and MCP collaborative optimization, and relates to the technical field of intelligent scheduling, wherein the task execution method based on agent and MCP collaborative optimization comprises the following steps: acquiring a predefined graph structure, wherein the graph structure comprises one or more candidate super edges of each type of task, and the nodes in the candidate super edges are model context protocol (MCP) tools or agents for task execution; receiving a task instruction input by a user, and acquiring a context feature vector according to the graph structure and the task instruction; determining the matching scores of each candidate super edge in the graph structure according to the context feature vector; comparing the matching scores of the candidate super edges, and determining a target super edge from the candidate super edges; calling the MCP tools and / or agents corresponding to the target super edge to execute the task instruction, obtaining a task execution result, and realizing intelligent collaboration between multiple agents and MCP tools, thereby efficiently executing the task.

This invention belongs to the field of radar information processing technology and discloses a pre-training method for a multimodal model of radar target detection, tracking, and recognition. The invention includes collecting model pre-training data; converting the collected radar information processing data from various models into a unified data format through data preprocessing to serve as input data for the pre-trained model; achieving simultaneous input of multimodal data, including images, tracks, and category text, through a unified pre-training framework, and completing joint representation learning of the input data by the pre-training framework; and designing a multi-task joint learning optimization algorithm to achieve parallel training of radar target detection, tracking, and recognition tasks. This invention achieves integrated processing of radar target detection, tracking, and recognition tasks by establishing a multi-task learning mechanism under a unified model training framework, effectively improving the overall performance of radar information processing. Simultaneously, by enhancing versatility and generalization, the model is applicable to intelligent processing tasks of various radar models.

The invention relates to the technical field of bionic robots, in particular to a variable-structure snakelike modular triphibian cross-medium bionic specialized robot. The variable-structure snakelike modular triphibian cross-medium bionic specialized robot is composed of eleven functional modules, comprises a head master control module, a joint connection module, a ducted fan module and a tail module, has water, land and air triphibian movement capacity, is driven in a redundant mode through hybrid power, integrates bionic winding, wheel type rolling and a lift force ducted system, and is high in stability and high in reliability. On the basis of CPG distributed control, environment adaptation and path planning are achieved in combination with a visual sensing system, cross-medium switching can be autonomously completed, robustness is improved through modular design, and the robot is suitable for complex environment operation and has high propulsion efficiency and task completion capacity.

The invention provides an unmanned aerial vehicle group self-organizing task collaboration method and device, electronic equipment and a storage medium, and the method comprises the steps: building a distributed service list based on capability services based on respective capability services shared by each unmanned aerial vehicle in real time; determining a group leader unmanned aerial vehicle based on a task request message of the ground station, and screening task group members according to capability service quality provided by the group leader unmanned aerial vehicle; processing the task request message based on a task allocation algorithm to obtain a plurality of sub-tasks, unmanned aerial vehicle groups corresponding to the sub-tasks and a task allocation table; according to the unmanned aerial vehicle group self-organizing task coordination method, unmanned aerial vehicle group self-organizing task coordination is completed by executing the corresponding sub-tasks through the unmanned aerial vehicle groups receiving the task allocation table, and compared with direct control of the unmanned aerial vehicle group through a ground station, task-oriented unmanned aerial vehicle group command control can be achieved, the unmanned aerial vehicle group task autonomy capability is improved, and the unmanned aerial vehicle group self-organizing task coordination efficiency is improved. The unmanned aerial vehicle group task efficiency is enhanced; and the burden of control personnel is reduced.

The present application relates to the technical field of artificial intelligence, and particularly relates to a model fine-tuning method for privacy protection, comprising the following steps: S1, obtaining a basic model to be fine-tuned and injecting an initial low-rank adapter containing a dimension reduction matrix and a dimension increase matrix, and taking parameters of the initial low-rank adapter as current low-rank adapter parameters; S2, obtaining local original training data containing real labels, identifying privacy identifiers in the local original training data and replacing the privacy identifiers with placeholders to obtain training data containing local target training samples and the real labels. In the present application, through clipping and Gaussian noise processing of single-sample gradient in the local training stage and in combination with data desensitization preprocessing, the local original training data does not leave the local client in plaintext form in the whole process of participating in model fine-tuning, so that effective privacy protection is provided for sensitive data of users in a federated distributed training scene.

This invention provides a lightweight adaptation method and system for large models. By extracting the semantic core from educational data, domain topic vector clusters are obtained, and pseudo-instruction-response pairs are generated to expand the training set based on these clusters. In terms of model structure, a low-rank adapter module is inserted into the transformer layer of the pre-trained large model, and the semantic direction of the topic vector clusters is extracted through singular value decomposition. The adapter parameters are initialized, and a weighted strategy is adopted during training: the prediction entropy and cross-entropy loss of each sample are calculated, and sample weights and gradient adjustment coefficients are generated based on these. The gradient is scaled and updated, and orthogonality constraints are applied, with the strength of the constraints adjusted by the sample weights.

The application relates to a Hilbert fractal curve-based image convolution method and system, comprising the following steps: 1) performing a padding operation on an input image according to a convolution output size mode, for example, the input size is equal to the output mode, and a padding operation needs to be performed on the input edge; 2) block index and rearrangement: performing image block reading and rearrangement operations on the padded input image according to the index sequence number of the Hilbert fractal; 3) cross-correlation operation: performing convolution cross-correlation operation on the rearranged image block with a step equal to the convolution kernel size; 4) local feature aggregation: performing convolution cross-correlation operation on the output of step 3 with a step of 1 by using a deep separable one-dimensional convolution; 5) restoring the two-dimensional structure of the input: performing reverse rearrangement operation on the output of step 4 according to the index of the Hilbert fractal, and outputting. Compared with the existing image convolution operation method, the application is simple, has strong universality and has certain theoretical advancement.

The application belongs to the technical field of aircraft trajectory optimization, and discloses an intelligent generation method of reentry trajectory of an aircraft based on a RBF neural network. Firstly, a reentry model of the aircraft is constructed, and a trajectory optimization problem to be solved is constructed based on the reentry model; then, an optimal sample trajectory set is generated by using a pseudospectral method according to initial discrete altitude and speed; after that, a sample trajectory is generated according to a new discrete state at each waypoint. Then, the sample set is divided according to the waypoints, and the RBF neural network is trained respectively; the input of the RBF neural network is the discrete altitude and speed state, and the output is a state-control sequence from the current state to the target point. Finally, the RBF neural network is used to generate an optimal trajectory online. The simulation verifies that the confidence of the method is high and the error is small, and the generation efficiency of the optimal trajectory can be greatly improved.