12 results about "Decision area" patented technology

The invention discloses an anti-fact generation method for processing class imbalance based on a real sample. The method comprises the following steps: preprocessing input data; performing causal feature selection by adopting a causal discovery algorithm; calculating causal feature tendency scores, and performing matching; carrying out anti-fact generation, forming a synthesized minority class set, and integrating the synthesized minority class set with original data to obtain an enhanced data set; and performing data cleaning on the enhanced data set to obtain a balanced data set. According to the method, a data set is effectively balanced by generating a high-quality and close-to-reality anti-fact sample, so that the performance of a downstream classifier on key indexes is remarkably improved; the feature values of the real instances are combined to ensure that the generated samples are located in a reasonable area of data distribution, so that the credibility and availability of the enhanced data are improved; the generated anti-fact sample is located in a boundary region between the majority class and the minority class, the decision region of the minority class is effectively expanded, and the unique post-cleaning avoids the influence of noise accumulation on model training.

In the field of image recognition, small sample class incremental learning of deep learning model is a challenging technical problem, which iteratively updates parameters based on pre-trained model, so that the model can continuously learn the knowledge of new classes only with a limited number of labeled samples, while retaining the knowledge of original classes. Parameter updating under extremely limited samples will cause the model to deviate, and a large number of parameter updates will cause the model to forget existing knowledge. In order to solve the above problems, on the basis of nearest neighbor class mean classification method, the application proposes a small sample class incremental learning method based on robustness and few parameters fine-tuning. The method can realize efficient fine-tuning of key parameters, and produce flexible decision region by controlling the number and amplitude of parameter adjustment, thereby avoiding the above problems.

The invention relates to the technical field of building information management, in particular to a data-driven artificial intelligence aid decision-making method and system, and the method comprises the steps: recording project data with conflicts based on a division mode of a building life cycle; based on the date attribute, the distribution attribute and the site attribute of each building component in the project data in each stage, defining a conflict feature matrix of each building component according to the conflict type of each building component; taking the conflict feature matrix of each building component as a node, performing spatial clustering on each node, and constructing a decision work flow corresponding to each building component; performing time sequence association on the decision work flow, deducing decision intervals, and constructing a multi-level decision tree by project data in each decision interval; performing collaborative analysis by using a multi-level decision tree, determining a decision path output by the decision tree, and obtaining a currently executed construction processing strategy; the efficiency and accuracy of conflict data processing are realized.

The present application relates to the technical field of building information management, in particular to a data-driven artificial intelligence assisted decision-making method and system, comprising: recording project data with conflicts based on the division mode of building life cycle; defining the conflict feature matrix of each building component based on the date attribute, allocation attribute and site attribute of each building component in each stage in the project data, and the conflict type of each building component; taking the conflict feature matrix of each building component as a node, spatially clustering each node, and constructing the corresponding decision-making workflow of each building component; time-sequentially associating the decision-making workflow, deducing the decision-making interval, and constructing a multi-level decision tree with the project data in each decision-making interval; using the multi-level decision tree for collaborative analysis, determining the decision path output by the decision tree, and obtaining the current construction processing strategy; the efficiency and accuracy of conflict data processing are realized.

The present disclosure provides a low-orbit satellite communication resource allocation method, device, equipment and medium, the method comprising: receiving service request signaling sent by a ground user; obtaining satellite state information at the current time according to the service request signaling; inputting the satellite state information into a pre-deployed multi-agent deep reinforcement learning model to generate a resource allocation scheme for the next time; wherein the multi-agent deep reinforcement learning model comprises a first decision area and a second decision area, the first decision area is used to determine a beam scheduling scheme for the next time based on the satellite state information, and the second decision area is used to determine a frequency band and power allocation scheme for the next time based on the beam scheduling scheme for the next time. According to the embodiments of the present disclosure, real-time perception and accurate response to uneven user distribution and time-varying traffic characteristics are realized, and the dynamic adaptation capability and overall operation efficiency of the low-orbit satellite communication system resource allocation are effectively improved.

The invention discloses a table data multi-model multiplexing method based on dynamic path selection, which comprises the following steps of: collecting a table data set in a corresponding field on a website or a platform, preprocessing the table data set, and dividing the preprocessed table data set into a training set and a test set according to a proportion; a deep neural network model is constructed, initialized and trained, the deep neural network model comprises an input module and a plurality of mixed expert layers, a total loss function is defined for iterative training until the deep neural network model converges to obtain a multi-path model for storage, and a part of the multi-path model is stored to represent a training sample and a reasoning path library. And loading a multi-path model to process the to-be-processed table data sample, dynamically generating a reasoning path, outputting a prediction result, and establishing a multiplexing library based on a historical path. According to the method, the advantages of multiple models can be integrated without extra training cost, the calculation cost is reduced and the table data processing performance is improved while the decentralized decision-making area of the tree base model is duplicated, and the resource consumption is saved while the performance is improved.

This invention discloses a system and method for optimizing unstructured data join queries based on Siamese networks, belonging to the field of unstructured data processing. It includes: a feature extraction module, which uses a deep learning model to extract feature vectors from different types of unstructured data; a Siamese network module, which converts the feature vectors into a low-dimensional embedding space; a judgment layer module, which concatenates the feature differences and the original features, and outputs the join matching probability through a fully connected network; a threshold decision module, which determines the optimal upper and lower threshold pairs through adaptive mapping and grid search, and divides the join matching probability into an automatic decision area and a verification area, with the verification area further judging the join relationship through a dedicated inference model; and a query optimization module, which optimizes the query execution plan for unstructured data based on the join matching results. This invention enables efficient unstructured database join operations, supports automatic decision-making and precise verification mechanisms, and significantly improves the accuracy and efficiency of database query join operations.

The invention discloses an equivalent verification method and device of a quantum circuit, equipment and a storage medium. Comprising the steps of obtaining an initial decision diagram through a first quantum circuit and a second quantum circuit; carrying out one-way endpoint movement based on a decision starting interval in the initial decision diagram, and storing the obtained left decision diagram and right decision diagram into an unselected decision diagram set; selecting a decision diagram with a larger decision estimation value from the unselected decision diagram set according to a target estimation function as a target decision diagram; when the target decision interval of the target decision diagram does not cover the initial decision interval of the initial decision diagram, updating the target decision interval as a start decision interval to obtain the left decision diagram and the right decision diagram again to determine a new target decision diagram until the target decision interval covers the initial decision interval, and obtaining a minimum decision diagram; and when the node merging result of the minimum decision diagram is a unit matrix, outputting circuit equivalent information. According to the method, the calculation complexity of quantum circuit equivalent verification can be reduced, and the calculation efficiency is improved.

This invention relates to the field of information system detection technology, specifically disclosing an artificial intelligence-based method for detecting anomalies in information system operation. The method includes: dividing the historical intelligent decision-making chain of an auctioned property into decision interval categories, statistically analyzing the proportion of anomalous decision points in each interval, calculating the confidence level anomaly probability, setting different detection window lengths, and then using the detection window length as a fixed unit to slide and extract the decision chain to be judged to generate a window to be judged. Continuous anomalies within the judgment window are identified, and new anomalous decision points are marked. The window to be reviewed is identified by combining the interval confidence level anomaly probability. Subsequently, the offline due diligence results chain is linked to compare and verify the validity of the anomalies, distinguishing between misjudgments and genuine anomalies. Finally, the initial detection window length is dynamically adjusted based on the time characteristics of the anomalous decision points to obtain the final detection window length, thus achieving dynamic adjustment of the detection window length and improving the accuracy of anomaly detection and risk control capabilities.

The invention discloses a model fingerprint method based on identification decision area differences, and belongs to the technical field of multimedia information security. According to the technical scheme, a positive model pool is constructed for a source model by using a plurality of model stealing methods, and a negative model pool is constructed for another independent training model by using a model extraction model stealing method and a re-independent training method; performing iterative optimization on the clean image to generate a model fingerprint query set of the source model; evaluating a fingerprint sample by using the calculated transferability score, and performing model fingerprint screening on the query set; and querying the suspicious model by using the model fingerprint query set, analyzing a result, and performing model fingerprint verification. The method has the beneficial effects that the method is excellent in the aspects of improving the model fingerprint construction speed, enhancing the copyright verification capability, protecting the model intellectual property, improving the model security, reducing the copyright verification cost and the like; the method not only provides powerful technical support for intellectual property protection of the deep learning model, but also promotes healthy development of the artificial intelligence technology.

1. The name of the design product: auxiliary diagnosis decision area of large model auxiliary diagnosis decision graph user interface for electronic equipment. 2. The use of the design product: for an electronic device. 3. The design points of the design product: in the graphical user interface. 4. The picture or photo that best indicates the design points: front view. 5. The use of the graphical user interface: overall use: interface for large model lesion staging intelligent auxiliary diagnosis; local use: auxiliary diagnosis decision area for large model. 6. The human-computer interaction mode of the graphical user interface: the front view is a lesion staging auxiliary judgment function interface, clicking the "start generation" button on the right side of the interface enters the lesion staging version generation interface, as shown in the change state diagram, the staging version can be switched to regenerate in the interface. 7. Other circumstances that need to be explained: omit other views. The partially protected design is not requested to be protected in the semi-transparent yellow covered part in the view. The "X" in each view represents replaceable or changeable text, numbers or punctuation marks; the part covered by the opaque gray block in each view belongs to the replaceable or changeable file content, which does not belong to the content of the design itself which is requested to be protected.