7results about How to "Incomplete solution" patented technology

The invention discloses a law and regulation retrieval method based on knowledge graph multi-hop reasoning query, a medium and equipment. The method comprises the following steps: firstly, constructing a finance and tax knowledge graph and a vector database; after a query request is received, tax labels and tax concepts are extracted, and a first candidate fragment set is obtained through vector retrieval. And performing one-hop query on the candidate fragment sets in the knowledge graph to obtain associated tax concept nodes and filter the associated tax concept nodes to form an extended tax concept set. Performing one-hop query by taking the extended tax concept set as a starting point to obtain more associated regulation fragments to form a second candidate fragment set; and finally, combining the two candidate fragment sets, and carrying out sorting and screening according to the law and regulation effectiveness, the release time and the semantic similarity to generate a structured final retrieval result. According to the method, through two-time directional query and intermediate concept filtering, the problems of incomplete laws and regulations recall and irrelevant information interference in a multi-hop reasoning scene are effectively solved.

Embodiments of the present application disclose an information determination method, which comprises: obtaining a plurality of sample data of a sample abnormal program, wherein the sample data represents corresponding running data of the sample abnormal program at different times during a running process of the sample abnormal program; performing model training on an initial real-time feature extraction model and an initial space feature extraction model based on the sample data to obtain a target real-time feature extraction model and a target space feature extraction model; obtaining a target classification model based on the target real-time feature extraction model and the target space feature extraction model; processing target running data of a to-be-detected program by using the target classification model and a target classifier to determine whether the to-be-detected program is abnormal; and solving the problem of incomplete feature information extracted in related technologies, thereby improving the accuracy of determining an abnormal program. Embodiments of the present application also disclose an information determination device, equipment and a computer readable storage medium.

The application discloses a passive radar network signal cooperative complementary sorting method and system, wherein the method comprises the following steps: cooperatively complementing mixed alias radiation source pulse sequence signals received by multiple passive radar stations to obtain complemented pulse sequence signals; and sorting the complemented pulse sequence signals by using a cumulative difference histogram method to separate pulse sequences of different radiation sources. Compared with an existing single-station passive radar signal sorting method, the method solves the problem of incomplete pulse sequences caused by pulse loss, greatly improves the correctness of signal sorting, and has better sorting performance, especially when pulse loss is serious.

The application relates to the technical field of automatic film replacing, and discloses an automatic film replacing device for a front bumper of an automobile, which comprises a workbench, a fixing frame arranged on the workbench and used for fixing the front bumper, and a film replacing assembly which is movable relative to the front bumper; the film replacing assembly comprises a rotating conversion cylinder, a hot film taking assembly, a hot treatment and mark cleaning assembly, an extrusion drying assembly and an extrusion film applying assembly which are arranged around the rotating conversion cylinder and used for film treatment; the hot film taking assembly, the hot treatment and mark cleaning assembly, the extrusion drying assembly and the extrusion film applying assembly can rotate with the rotating conversion cylinder; the workbench is further provided with an alignment assembly which is used for aligning the hot film taking assembly and the hot treatment and mark cleaning assembly with the front bumper; and the device solves the problems that the traditional automobile film pasting method cannot completely paste the film on the front bumper of the automobile and the film replacing is troublesome.

The invention discloses a classroom facial expression recognition method based on an improved RT-DETR model. The method comprises the following steps: 1, obtaining a data set of classroom facial expression images; 2, preprocessing the data set by using a computer; 3, training a data set by using a classroom facial expression recognition detection model based on an improved RT-DETR model; 4, training the classroom facial expression recognition detection model to obtain an optimal detection model; and 5, performing real-time identification detection on the classroom facial expressions of the students in combination with monitoring and an optimal detection model. According to the invention, the CAFI module is designed in the backbone network, so that the classroom facial expression features can be extracted and screened more accurately; an MCFEEConv convolution feature enhancement module is designed in the head network, and the feature expression ability of faces with different distances and different sizes is improved; a RepC3LWGA module is designed in a head network, and joint modeling of local convolution features and global context features is achieved.

This invention relates to a method for completing travel chain data based on an improved Radial Basis Function (RBF) neural network model, belonging to the field of intelligent transportation technology. The method includes: acquiring a user's current travel chain data, wherein the current travel chain data includes at least one travel segment and at least one missing travel segment, the travel segment including public transportation data, and the missing travel segment including walking or cycling data; inputting the user's current travel chain data into a pre-trained improved RBF neural network model to obtain complete travel chain data, wherein the pre-trained improved RBF neural network model is trained based on historical travel chain data, including historical mobile phone signaling data and historical public transportation card swipe data. Through multi-source data fusion and an improved radial basis function (RBF) neural network model, walking or cycling data in the travel chain is automatically identified and completed.

The invention provides a platform construction method based on a renewable energy agent and a system optimization network, and belongs to the technical field of renewable energy technologies and artificial intelligence. The method comprises the following steps: constructing an agent model covering multiple types of renewable energy such as wind power, photovoltaic power and hydropower, integrating multi-source energy data by using a knowledge graph, and constructing a system optimization network; and dynamic regulation and control of the system-level optimization network are realized in combination with deep reinforcement learning. The platform comprises a multi-source energy data acquisition and fusion module, a renewable energy agent modeling module, a system optimization network construction module, a multi-energy collaborative decision module and a dynamic evaluation feedback module. According to the method, the problems of insufficient multi-energy complementarity mining, low dynamic optimization precision, poor decision collaboration and the like in the existing renewable energy system are solved, and the comprehensive utilization efficiency and the regulation and control intelligence level of the renewable energy system are remarkably improved.