10results about How to "Comprehensive description" patented technology

The present application provides an asphalt pavement apparent disease evolution deduction method, system, device and medium, which belongs to the field of road engineering, transportation infrastructure operation and maintenance and intelligent detection technology, and comprises the following steps: constructing a unified space-time reference system, aligning and normalizing multi-source data in space-time; forming section-level disease quantification characteristics based on the recognition results of the inspection images and the structural indexes; event coding the maintenance measures of the road surface and pre-processing the section-level disease quantification characteristics corresponding to the time of the maintenance measures; constructing the correlation, time lag effect and spatial consistency between the disease types, disease characteristics and structural indexes to generate the collaborative evolution characteristics of the coupling relationship between diseases; constructing a deep time series prediction model to obtain the disease development trend at a specified time scale in the future, and forming the section-level disease evolution prediction result. The present application can depict the disease development law at different road sections and different time scales, and predict the future disease expansion.

The present application relates to the technical field of agricultural product origin identification and traceability, and particularly discloses an agricultural product origin identification and traceability system and method based on characteristic fingerprints, which comprises the following steps: collecting agricultural product samples to obtain a target sample set; marking the target sample set with dominant characteristic fingerprints according to the dominant characteristics of the agricultural products, and performing one-class partitioning on the target sample set according to the dominant marking result to obtain a plurality of one-class sample clusters; marking the target sample set with recessive characteristic fingerprints according to the recessive characteristics of the agricultural products, and performing two-class partitioning on the target sample set according to the recessive marking result to obtain a plurality of two-class sample clusters; comparing the one-class partitioning result and the two-class partitioning result to determine whether to start a successive verification mode or a separate verification mode; based on the successive verification mode, obtaining each verification result, and determining whether to output an actual origin traceability result according to the verification results.

The invention relates to the technical field of power dispatching, and discloses a power dispatching method and system based on artificial intelligence. The method comprises the following steps: collecting power grid operation state data which is uploaded by a terminal and comprises node operation parameters and regional pre-judgment working condition types, and receiving real-time power grid topology connection data of a geographic information platform; performing space-time correlation analysis on the node operation parameters and the regional pre-judgment working condition type to obtain node operation state correlation characteristics and working condition pre-judgment correlation characteristics; optimizing the node operation state association features according to the real-time power grid topology connection data to obtain optimized node state topology features; dynamically integrating the working condition pre-judgment association features and the optimized node state topological features, and generating a power grid operation working condition fusion feature representation; and generating a power grid operation mode identifier based on the fusion feature representation, matching a preset regulation strategy from a strategy library, and issuing the preset regulation strategy to a corresponding acquisition terminal. According to the method, effective integration and analysis of multi-source data are realized, and the accuracy and adaptability of power dispatching are improved.

This invention discloses a method for estimating threat events in a metaverse network, primarily addressing the problems of existing threat detection methods failing to fully reflect the dynamics of the system, lacking the ability to detect real-time network attacks, and lacking the capability to reconstruct attack scenarios. The implementation scheme involves: monitoring changes in virtual resource parameters within the metaverse virtual environment and issuing warnings about the current virtual boundary status of the user; constructing a source map using system log data collected from the user's device and network space anchor entity information; compressing the source map to obtain a compressed source map, which is then visualized in the Neo4j database; setting attack stage labels for entity matching in the compressed source map, and performing forward and reverse searches on the compressed source map to extract the attack map of metaverse security boundary attacks. This invention can detect attacks in the metaverse network in real time, has a wider range of applications, enhances the ability to proactively respond to metaverse network threats, and can be used for network security.

The application discloses a kind of street view space multimodal fusion intersection scene safety risk quantification method, based on historical traffic accident data and considering distance attenuation effect, the traffic accident occurrence intensity index of intersection scene area is calculated and as training label data;Based on street view panoramic image and road network data, the visual features of intersection scene are calculated, the spatial structure features of intersection scene are calculated;Based on interest point and traffic flow data, the semantic features of intersection scene are calculated;The above-mentioned features are combined to construct intersection scene safety risk feature vector;Intensity index and safety risk feature vector are combined, and the safety risk quantification model of intersection scene is constructed using random forest regression algorithm.The application designs traffic accident occurrence intensity index, and models are considered from the visual, spatial structure and semantic dimension features of intersection scene, which has low dependence on computing power and label data, strong operability, and can provide technical support for road safety risk early warning and safe city construction.

The application discloses a brain region target point division method for autism neural regulation, which comprises the following steps: acquiring resting-state functional magnetic resonance imaging data of an autism patient, and pre-processing the functional magnetic resonance imaging data; extracting back-lateral prefrontal cortex ba9 and ba46 in the pre-processed data as target brain regions by using a Brodmann template; unfolding the pre-processed image data into a two-dimensional time sequence, and performing sliding window processing on the time sequence to obtain a plurality of sliding window time sequences; calculating a functional connection matrix of voxel points in the target brain region and all voxel points in the whole brain gray matter in each sliding window time sequence; performing dimension reduction processing on all the functional connection matrices based on local similarity to obtain a reduced dimension functional connection matrix; acquiring a reference clustering template of a normal person which has been constructed, performing Kmeans clustering on the functional connection matrix of the autism patient according to the clustering cluster number, and obtaining sub-region division of the target brain region.

The application provides a background error covariance matrix generation method and device, a terminal and a storage medium, and relates to the technical field of numerical weather prediction. The method comprises the following steps: constructing a short-term prediction sample set of a target numerical weather prediction, and extracting a control variable of each background error sample in the short-term prediction sample set; calculating a regression coefficient of each control variable to construct a balance operator; inputting the short-term prediction sample set into a constructed characteristic length scale field generation model, outputting a characteristic length scale field of each background error sample, and constructing a horizontal correlation operator; calculating a vertical correlation scale of each control variable at each horizontal position to construct a vertical correlation operator; calculating a background error standard deviation of each control variable at each grid point to construct a standard deviation operator; and obtaining the background error covariance matrix by using the balance operator, the horizontal correlation operator, the vertical correlation operator and the standard deviation operator. The application can reduce the calculation complexity of the horizontal correlation operator and improve the calculation efficiency.

The present application relates to the field of electric meter remote monitoring, more particularly, the present application relates to the electric meter remote fault diagnosis method and system based on internet of things, the method comprises: obtaining all historical fault sequences corresponding to each fault type; the period of each historical fault sequence is calculated and the probability of each period appearing, and based on the historical fault sequence corresponding to a single period, the template sequence of the period is generated; the real-time parameter time sequence of the electric meter is collected, the similarity of the real-time parameter time sequence and the template sequence of each period corresponding to the fault type is calculated, the calculated similarity is weighted and summed according to the probability of each period appearing, and the real-time fault probability of the fault type is obtained; when the real-time fault probability is greater than the preset fault threshold, the diagnosis result of the corresponding fault type of the electric meter is output. The present application can more comprehensively and accurately describe the complex evolution law of the fault, and realize the remote and timely diagnosis of the electric meter fault.

The invention discloses an enterprise industry classification method and system based on associated enterprise information and a BERT model, and relates to the technical field of computers. The method comprises the following steps: acquiring enterprise information of a to-be-classified enterprise; enterprise keywords of the to-be-classified enterprises are extracted according to the enterprise information, and an enterprise keyword set of the to-be-classified enterprises is determined based on the enterprise keywords; respectively calculating the similarity between the enterprise keyword set and each preset industry keyword set through a pre-trained BERT model; the industry categories corresponding to the industry keyword sets with the similarity larger than a similarity threshold value are selected as alternative industries to which the industry categories belong; if the alternative industry is not unique, determining an associated enterprise of the to-be-classified enterprise, processing the associated enterprise information of the associated enterprise and the similarity of each alternative industry according to a preset comprehensive industry score calculation rule, and calculating to obtain a comprehensive industry score of each alternative industry; and the industry with the highest score is selected as the belonging industry, so that the accuracy of enterprise industry classification is improved.

The invention provides an asphalt pavement apparent disease evolution deduction method, system, equipment and medium, and belongs to the technical field of road engineering, traffic infrastructure operation and maintenance and intelligent detection, and the method comprises the steps: constructing a unified space-time reference system, and carrying out the space-time alignment and normalization of multi-source data; based on an identification result of the inspection image, combining the structural indexes to form section-level disease quantitative features; carrying out event coding on the maintenance measures of the pavement and carrying out feature preprocessing on the section-level disease quantitative features at the moments corresponding to the maintenance measures; constructing correlation among disease types, disease characteristics and structural indexes, and generating co-evolution characteristics of a coupling relationship among the diseases according to a time-lag effect and space consistency; and the constructed depth time sequence prediction model obtains a disease development trend of a specified time scale in the future, and forms a section-level disease evolution prediction result. According to the method, the disease development rule can be described in different road sections and different time scales, and future disease extension is predicted.