11results about How to "Avoid homogenization" patented technology

The invention discloses a power distribution network topology identification method based on ARAM graph convolution, and the method comprises the steps: 1, taking lines in a power distribution network as nodes, taking the lines with power connection between the lines as edges, and abstracting the power distribution network as a line graph structure; 2, generating various types of topological structures according to the basic topological structure of the power distribution network system, and establishing a data set by taking electrical quantities such as voltage difference of a power distribution network line as node characteristics of a line graph and taking an on-off state vector of the line as a label; 3, constructing an ARMA-based graph convolutional network model for power distribution network topology identification; 4, training an ARMA-based graph convolutional network model and storing the trained model; and 5, collecting real-time measurement data of the power distribution network, and inputting the normalized data into the trained optimal ARMA graph convolutional network model to carry out real-time identification on the topology of the power distribution network. According to the method, accurate and real-time topology identification can be realized in a large-scale power distribution network with uncertain scenes such as distributed renewable power supply fluctuation.

The invention relates to the technical field of digital product data processing, and discloses a digital product recharging and selecting system which comprises a data acquisition module, a feature extraction module, a score calculation module, a score compensation module and a commodity recommendation module. Compared with the prior art that a digital product is subjected to static recommendation only based on historical consumption behaviors of a user, and particularly, real-time dynamic optimization recommendation cannot be realized under the condition of rapid change of user preferences or cold start of new commodities, the method and the device have the advantages that the user behavior feature sensitivity index and the real-time interaction behavior are combined, so that the real-time dynamic optimization recommendation can be realized; according to the method, the weight of the recommendation model is dynamically adjusted, accurate capturing of user requirements and real-time updating of the recommendation sequence are achieved, therefore, the problems that in a traditional recommendation system, recommendation results are homogenized, and the real-time performance is insufficient are solved, and the accuracy of digital product recommendation is improved.

The invention discloses a content recommendation method and device, equipment, a medium and a program product, and relates to the field of content recommendation, and the method comprises the following steps: receiving equipment connection information uploaded by first equipment, the equipment connection information being information which is collected by the first equipment and is related to second equipment when the second equipment is connected with the first equipment, the first device and the second device are connected through a short-distance wireless transmission connection mode, and a first account is logged in the first device; first content is obtained based on the device connection information, the first content is to-be-recommended content, and the first content comprises elements associated with the device connection information; and pushing the first content to a third device logged in with the first account, wherein the third device is the same as or different from the first device. Recommended contents can be determined based on equipment connection information, and the content recommendation effect is improved.

This invention relates to the field of image processing technology, specifically disclosing a zero-shot text attribute graph learning method based on prototype mutual prompting enhancement. The method calculates instance-aware contrast loss and constrains the similarity of the same node's embedding in two views. It performs PCA dimensionality reduction on the token embedding of the LLM, extracts the first P principal components to form an aligned coordinate system C, maps the components to this coordinate system, and calculates the informative-aware contrast loss. It freezes the pre-trained GNN and the LLM backbone network, extracts node structural embeddings Z from the pre-trained GNN, generates K prototype embeddings through K linear projectors, and injects them as soft prompts into the LLM instructions. The LLM outputs K expert prompts. It calculates the weights of each expert prompt through a router model. It freezes the LLM training of related projectors and routers, freezes the GNN training of the LLM prompt adaptation layer, and iterates and optimizes 1-3 times. This method can cover complex cross-modal information in text and images while reducing the accumulation of zero-shot transfer bias.

The embodiment of the present application discloses a shared power bank control method, a shared power bank cabinet, and a computer-readable storage medium. The method includes the following steps: when a charging application is detected, the model information of the mobile device that needs to be charged is obtained; the type of power bank is determined according to the model information, and the type of power bank is used to indicate the charging method supported by the charging interface of the power bank for charging the mobile device; the power bank that meets the pop-up conditions is selected according to the type of power bank, and the power bank is controlled to pop up. Therefore, the present application can output the power bank according to the model information of the mobile device, and the power bank that meets the charging interface type of the mobile device is output accordingly, thereby avoiding the waste of resources in manufacturing universal power banks. At the same time, a power bank has at least one charging interface, which saves manufacturing costs or transformation costs compared to the prior art of setting multiple interfaces, reduces the difficulty of manufacturing new shared power banks, and makes the shared power bank business easier to promote and popularize.

The utility model discloses a fillet clamping box which comprises a first paperboard and a first arc plate and is characterized in that the first paperboard is fixedly connected with the first arc plate, one side of the first arc plate is fixedly connected with a first side plate, and the upper surface of the first side plate is fixedly connected with a first sealing plate; and the lower surface of the first side plate is fixedly connected with a second sealing plate. According to the rounded corner clamping box, through the arrangement of the first paperboard, the first arc plate, the first side plate, the first sealing plate, the second sealing plate, the first wall plate, the folded plate, the first double-faced adhesive tape, the second wall plate, the second double-faced adhesive tape and the second arc plate, in the using process, the first side plate and the second side plate are folded upwards firstly, and the first arc plate and the second arc plate are driven to move; then the mold can be put in, the second paperboard drives the third arc plate to be folded, buckled and attached, the third side plate drives the fourth arc plate to be folded downwards and buckled on the inner side of the first side plate, and the mold is taken out.

The application provides a special management method and system for an education research platform, which comprises the following steps: checking the personnel identity of each logged-in person respectively, issuing corresponding research tasks and research permissions, obtaining the permission exercise data generated when each logged-in person executes the research task, constructing the research archives of each logged-in person in combination with the corresponding research permissions, constructing the research access path of the corresponding logged-in person in the research platform according to the permission exercise data, obtaining the corresponding research results of this time and transmitting them to the corresponding login page for display, analyzing the research reports of all the logged-in persons under different disciplines, and transmitting them to the platform homepage for display. The learning plan for each student in the education research platform is formulated, and the learning effect is analyzed, so that the learning effect of the student in the education research platform is effectively improved.

This invention discloses a data analysis and prediction system based on deep learning, belonging to the fields of data analysis and artificial intelligence technology. The system includes a data acquisition module, a data preprocessing module, a feature extraction module, a deep learning prediction module, a result output module, a model optimization module, a data storage module, an anomaly monitoring module, a human-computer interaction module, and a permission management module. Through a unique multi-source data fusion preprocessing algorithm, an improved deep learning prediction model, and a dynamic optimization mechanism, it achieves accurate analysis and efficient prediction of multi-domain and multi-type data. Its prediction accuracy is improved by more than 15% compared to existing technologies, and the response latency is controlled within 500ms. It also possesses strong environmental adaptability and data security, and can be widely applied to data analysis and prediction scenarios in finance, industry, healthcare, and other fields.

This invention discloses a vehicle management method and platform system based on user profiles. It acquires real-time vehicle driving behavior and vehicle body change information; aggregates a large amount of vehicle risk control information to establish a vehicle risk control database and a statistical model; sets user profile preferences based on the statistical model and vehicle risk control information; selects specific vehicle risk control information from the vehicle risk control database based on user profile preferences; and provides specific vehicle risk control information. By comprehensively considering both user driving behavior and vehicle body changes, it helps screen users, thereby accurately matching vehicle management product positioning, enabling competitor comparison, revenue risk control, user recommendation, and precise matching. This truly reflects the matching of car insurance with accident rates, accurately reflects customer segmentation, helps avoid homogenization of car insurance products, helps optimize the relative ratio of operating costs and charges, and can also help users improve their driving habits to a certain extent.

The application provides a bedside respiratory evaluation and individualized treatment system and method based on AI intelligent interpretation, which comprises: a respiratory evaluation data acquisition unit, a scale module integrated with a built-in multi-dimensional evaluation system, and a device interface module compatible with multiple brands of trainers; a bedside workstation core interaction unit for multi-source data integration; an AI intelligent interpretation engine unit carrying a lightweight deep learning model, which extracts features through a CNN-RNN fusion model and an attention mechanism, and generates diagnostic opinions in combination with a dynamic threshold library and a diagnostic reasoning module; an individualized treatment plan generation unit which generates a treatment plan containing training parameters and evaluation nodes through a multi-objective optimization algorithm; and an efficacy tracking feedback unit which acquires reevaluation data in real time and dynamically adjusts the scheme according to the efficacy analysis results, forming a closed-loop diagnosis and treatment mode. The application realizes the bedside integration of multi-source data fusion, AI intelligent interpretation and individualized treatment, and improves the diagnosis and treatment efficiency, consistency and accuracy.

The embodiment of the invention discloses a training method and device of a content understanding model, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the steps of obtaining a content sample and a query sample; inputting a plurality of sequence fragments of the query sample and the content sample into a to-be-trained model, predicting a plurality of groups of output samples in combination with global memory, each group of output samples including candidate memory and candidate answers; calculating composite rewards and advantage scores corresponding to the multiple groups of output samples; performing temperature sampling on the multiple groups of output samples to obtain a target output sample, and updating the global memory into candidate memory included in the target output sample; calculating a gradient corresponding to the sequence fragment; and accumulating the gradient corresponding to each sequence fragment, and updating the model parameters of the to-be-trained model to obtain a trained content understanding model. According to the scheme, the content understanding model with excellent performance can be efficiently trained at low cost.