55 results about "Real-time Search" patented technology

The invention provides a goods source recommendation method and device and electronic equipment, and belongs to the technical field of logistics transportation. In the method, a real-time search request of a target driver is received, and a first preset number of first historical click behavior data corresponding to the target driver is obtained; performing generalization processing on each piece of first historical click behavior data and the real-time search request to calculate a total generalization score, screening the first historical click behavior data according to a preset generalization score to obtain second historical click behavior data with higher relevancy with the real-time search request, and determining a second behavior sequence; and finally, inputting the second behavior sequence into the target recommendation model, and generating a recommendation result sorted according to the relevancy. According to the method provided by the invention, the long-term interest and periodicity rule of the driver can be modeled by effectively utilizing the second historical click behavior data with a longer coverage range and higher correlation with the real-time search request under the condition that the online reasoning performance is limited, and the accuracy of a goods source recommendation result is effectively improved.

Dynamic slice allocation in near real-time search platforms is described. A computing system may include a cluster including a plurality of nodes, each of the plurality of nodes including a plurality of slices storing searchable data. The query load distribution of the cluster is evaluated in real-time. A first slice of the first node and a second slice of the second node are identified based on the query load distribution. A first slice of a first node is commanded to be exchanged with a second slice of a second node.

The invention relates to the technical field of computer storage, and discloses a real-time search cache replacement method and system based on machine learning, and the method comprises the steps: obtaining multi-dimensional search data in the system, carrying out the data preprocessing of the multi-dimensional search data, building an LSTM (Long Short-Term Memory) network model, and carrying out the real-time search cache replacement through the LSTM network model. Using an improved whale optimization algorithm IWOA to perform parameter optimization on the LSTM long short-term memory network model, designing a multi-target reward function based on a PPO algorithm, using the multi-target reward function to optimize a current cache state and weight parameters of an online prediction model, inputting multi-dimensional feature real-time data into the prediction model to perform training, and obtaining a prediction result; meanwhile, the dynamic weight of the model is calculated, and target adjustment of the cache replacement rule is carried out based on an incremental data set. The anti-interference capability and the long-period learning efficiency of the cache system are obviously enhanced, and an intelligent cache solution with low delay and high resource utilization rate is provided for a high-concurrency scene.

The invention discloses a robot optimal path planning method based on an improved Hemma optimization algorithm. A plurality of improvement measures are provided for problems possibly existing in path planning of a Hemma optimization algorithm. Firstly, a Circle chaotic mapping strategy is adopted to replace random initialization, so that the quality and distribution uniformity of an initial population are improved, and the global exploration capability is enhanced. Secondly, mathematical models of three core behavior stages of a standard HO algorithm are deeply optimized, and adaptive dynamic weight, a dynamic weighting strategy based on a current optimal solution and individual fitness and guided Levy flight direction adjustment are introduced; and finally, introducing a dynamic stage conversion and adaptive exploration intensity mechanism DPTAEI, and dynamically adjusting the participation proportion of the individual in different behavior stages and the intensity of the exploration behavior according to the real-time search progress of the algorithm. Through the improvements, the optimization precision, the convergence speed and the global search capability of the IHO algorithm in the aspect of robot path planning are remarkably improved.

The present specification provides, amongst other things, a novel system, method and apparatus for real time searches. A search engine is provided that generates search parameters based on a natural language conversation with a chatbot. The parameters are parsed into a plurality of portions according to a refinement protocol. At least one of the portions is sent to a first engine for a search, and the results of are transformed using the refinement protocol.

The invention belongs to the technical field of Dijkstra algorithms, and discloses a Dijkstra algorithm and GAN model-based labyrinth game system, which consists of a labyrinth generation module, a path search module, a difficulty adjustment module, a personalized experience module, a feedback prompt module and a self-adaptive dynamic scheduling module. The dynamic generation of the maze map is realized by combining the generative adversarial network (GAN) and the Dijkstra algorithm, and the GAN can intelligently generate the maze layout adaptive to the style of the player according to the behavior data, historical performance and preference of the player, so that the repeatability of the map is avoided, the map can be dynamically adjusted according to the response of the player, the freshness and diversity are provided, and the user experience is improved. Meanwhile, through an intelligent difficulty adjusting mechanism, the performance of players is monitored and analyzed in real time, the difficulty of the maze is automatically adjusted, a Dijkstra algorithm is used for searching and optimizing a maze path in real time, and the Dijkstra algorithm can find the shortest path from the starting point to the end point in the maze.

Disclosed is a system and / or a method for near-instant trademark approval and rejection via ai-powered legal reasoning. The platform includes a user-facing interface enabling an applicant to input a proposed trademark comprising a word mark, logo, and slogan with a textual description of associated goods and services, and optionally visual proof of use in commerce. A classification engine powered by a natural language processing model assists in selecting classes of goods and services from standardized taxonomies. A backend examination engine performs real-time searches, analyses across databases of registered, pending, common law marks using a large language model, and design recognition algorithm. An autonomous legal reasoning module powered by the large language model interprets trademark law precedents, disclaimer requirements, registration criteria, and simulating multi-perspective legal analysis. An outcome generation engine delivers within a predefined time frame a preliminary approval with automated registration, preliminary refusal with detailed explanation, and recommended amendments.

Dynamic shard allocation in a near real-time search platform is described. A computing system may include a cluster comprising a plurality of nodes, each node of the plurality of nodes comprising a plurality of shards storing searchable data. A query load distribution of the cluster is assessed in real-time. A first shard of a first node and a second shard of a second node are identified based on the query load distribution. The first shard of the first node is commanded to exchange with the second shard of the second node.

Disclosed in the present invention are a live-broadcast monitoring processing method and system, and a device and a medium. The method comprises: performing data collection and aggregation processing of a whole live-broadcast process, so as to obtain monitoring source data, wherein the monitoring source data comprises monitoring data and historical data; using the monitoring source data as a training data set for model training processing, so as to obtain a large language model; performing anomaly detection processing on the monitoring source data by means of the large language model, so as to obtain a fault result; and performing real-time search and analysis processing on the fault result by means of combining the large language model and an internal search engine, so as to obtain an alarm handling scheme, wherein the alarm handling scheme comprises alarm trend information and a solution. By means of applying a large voice model and an internal search engine to a real-time monitoring system, and quickly processing and querying data, the embodiments of the present invention realize timely discovery and processing of anomalous data, and can be widely applied to the technical field of operation and maintenance monitoring.

Technology is disclosed for dynamically creating and / or validating new URLs based on a user's query and / or contextual data. This enables the discovery of relevant content that may not be pre-existing in the search engine's index. This process enhances the search engine's ability to deliver the most current and relevant results by generating potential content sources in real-time, validating them, and incorporating the discovered content into the index for immediate query execution. Various embodiments are additionally or alternatively directed to a process that involves iteratively discovering, crawling, and indexing new content by following links from seed URLs, which may be either pre-existing or generated. This process deepens the search engine's index by continuously exploring outlinks, fetching new content, and updating the index in real-time.

The invention provides a ship berthing auxiliary decision-making method and device, electronic equipment and a storage medium, and belongs to the technical field of ship berthing route control, and the method comprises the steps: obtaining a multi-modal environment and ship navigation sensing data; the method comprises the following steps: extracting carbon emission factors on the basis of a multi-modal environment and ship navigation sensing data, then constructing a carbon emission scoring model according to a nonlinear collaborative amplification effect among the carbon emission factors, and generating a low-carbon semantic map; a path evaluation function fusing a ship energy efficiency model and carbon emission-berthing time window compliance constraint is utilized, path section carbon emission and berthing time window compliance serve as a search basis, a berthing path enabling the path evaluation function value to be minimum is searched in real time, and the optimal navigational speed and steering angle are generated for each path section; therefore, ship berthing aided decision-making under low-carbon compliance guidance is realized. According to the method, comprehensive optimization of low-carbon property, compliance and performability can be realized, and the port and navigation system is assisted to be upgraded to a green and intelligent direction.

The invention provides a commodity search lexicon generation method and system in the technical field of search engine word segmentation, and the method comprises the steps: S1, obtaining a large number of historical search statements, carrying out the preprocessing and marking of each historical search statement, and constructing a data set; s2, creating a word segmentation model, and setting a loss function of the word segmentation model; s3, training the word segmentation model through the data set; s4, presetting a plurality of initial search words, and creating a commodity search word library based on each initial search word; s5, acquiring an input real-time search statement, performing word segmentation operation on the real-time search statement through the word segmentation model to obtain a plurality of real-time search words, and storing each real-time search word into a commodity search word library; and S6, real-time search behaviors corresponding to the real-time search statements are collected in real time, and the commodity search word bank is dynamically updated based on the real-time search behaviors. The method has the advantage that the word segmentation accuracy of the search word is greatly improved.

The invention provides a search method and device, electronic equipment and a medium, and relates to the technical field of artificial intelligence, in particular to the technical fields of natural language processing, deep learning, large language models and the like. According to the implementation scheme, configuration information of an intelligent agent and a target QA pair corresponding to the intelligent agent are obtained, and the target QA pair corresponding to the intelligent agent is pre-generated based on the following operation that candidate search requests associated with the intelligent agent are generated based on a user search pool and the configuration information, the user search pool comprises historical search records of historical users; generating a target answer corresponding to the candidate search request by using a large model, and forming a target QA pair corresponding to the intelligent agent with the search request; receiving a real-time search request of a current user; and in response to the condition that the real-time search request and the target QA pair meet the preset relationship, displaying the target answer to the current user and calling the intelligent agent to interact with the current user for the real-time search request.

Systems and techniques are provided for processing streaming data. For example, a process can include obtaining, by an edge device, a plurality of data streams corresponding to objects, activities, or events registered in an edge environment associated with the edge device. One or more machine learning networks implemented on the edge device can generate a plurality of features and / or embeddings corresponding to each respective data stream of the plurality of data streams. The edge device can determine a subset of salient content from the plurality of data streams, based on analyzing the plurality of features and / or embeddings generated for each respective data stream. The edge device can generate index information corresponding to the determined subset of salient content. The subset of salient content and the generated index information can be stored locally at the edge device for search and retrieval based on local queries received at the edge device.

The invention provides a formula editor implementation method based on a knowledge graph. According to the method, a formula editor is realized on the basis of the knowledge graph, operation is simplified through a semantic node relationship, a user can configure a formula only through scenario node selection without professional programming knowledge, and a use threshold is reduced by combining double strategies of dynamic sorting (association strength + use frequency) and fixed classification sorting and cooperating with a real-time search function; logic errors are verified in real time, and accuracy and configuration efficiency are improved by means of an AST disassembling formula structure and in combination with a formula library reuse function; business logic is dynamically defined, new functions do not need to modify codes, expansion can be achieved only by updating knowledge graph data, upgrading iteration is simplified, and maintenance cost is reduced; metadata accurately matches an editing scene, a front end displays a domain concept in real time, deep fusion of a business scene and formula editing is realized, logic deviation is avoided, and a reliable data basis is provided for decision making.

The invention discloses a traditional Mongolian yurt non-abandoned protection inheritance method based on an AI digital human, and relates to the technical field of artificial intelligence, and the method comprises the following implementation steps: configuring the IP address information of a non-abandoned culture remote control area server; entering a data detection end, ensuring that the non-perpetual culture accurately performs AI action display and explanation, and if the AI action is abnormal, searching culture contents in real time by combining with the Internet; entering a monitoring correction end, and tracking the action display and explanation content of the AI digital human in real time through a data tracking instrument; and entering an emotional identification end, and automatically matching an emotional demonstration picture of inheriting the cultural content for the AI digital person according to vocabularies. According to the traditional Mongolian yurt non-missing protection inheritance method based on the AI digital person, content deviation or data errors of display and explanation of the digital person to the Mongolian yurt non-missing are avoided, the experience and learning effect of audiences during AI digital person display are improved, and comprehensive and profound cultural experience can be obtained.

This invention belongs to the field of computer technology and discloses a financial information aggregation and recommendation method and system based on unsupervised cross-modal learning. It constructs a multi-channel data collection crawler engine to dynamically acquire heterogeneous data from cross-platform data sources; deploys a heterogeneous dual-engine architecture of semantic and numerical engines to perform semantic parsing and extract transaction features from the acquired heterogeneous data; constructs a cross-modal dynamic alignment matrix to generate a joint representation vector; acquires real-time search keywords and click behaviors to form short-term intent, analyzes historical cross-platform behavior trajectories to form long-term preferences, and constructs a two-layer intent recognition network based on short-term intent and long-term preferences; and outputs dynamic recommendation results integrating business objectives and user satisfaction indicators. This invention, based on unsupervised cross-modal learning technology, achieves cross-platform multi-channel financial information aggregation, deep user intent analysis, and personalized recommendation, thereby solving the problems existing in the prior art.

The invention discloses a mail system advanced threat traceability and detection method and system based on a dynamic attribute graph, and relates to the technical field of network security, and the technical scheme is characterized in that the method comprises the steps: collecting an operation log of a mail server in real time, and extracting a standardized field; based on a predefined graph ontology, mapping a log entity into nodes such as a user, a network address, a session, a resource and the like, mapping an operation behavior into a directed edge with a timestamp and a weight, and constructing a dynamic traceability graph; defining a malicious sub-graph topology template for a specific attack mode; searching matched connected sub-graphs in real time in the traceability graph by using a sub-graph isomorphic matching algorithm; and when matching succeeds, generating an alarm and extracting a sub-graph as a traceability evidence. According to the method, discrete log behaviors are correlated through a graph theory method, complex attack behaviors such as silence rule thunder burying, parasitic account aggregation and geographical kinetic energy abnormity can be effectively recognized, and the initiative and accuracy of mail security defense are remarkably improved.

The invention relates to the technical field of intelligent recruitment, in particular to a multi-platform candidate information intelligent synchronous search method and system. Comprising the steps that a candidate search request input by a user is obtained, and the search request comprises at least one search keyword; based on a preset multi-platform feature database, the real-time search weight of each recruitment platform is dynamically determined, and the multi-platform feature database collects and updates the candidate activeness of each recruitment platform, the platform attribute features and the historical search click rate of a user to each platform in real time; according to the real-time search weight, distributing the search request to a corresponding recruitment platform for synchronous search, and obtaining candidate information returned by each platform; performing multi-dimensional deduplication processing on candidate information returned by each platform, wherein the multi-dimensional deduplication processing comprises dominant feature matching and implicit feature semantic similarity calculation; according to the method, the candidate information acquisition quality and speed can be remarkably improved, and more reliable support is provided for enterprise recruitment decision making.

A visual search based real time e-commerce system and method with computer vision is disclosed. A visual search engine (150) of the said system draws item detection and classification (110) sub module to retrieve information from a remote database (118). The said item detection sub module (110) on the server having instructions, that when executed by a processor, cause operations to be performed, wherein the operations includes receiving at least one image (102) and / or a video uploaded (104) by a user using a mobile application to the said visual search engine (150). The visual search (150) engine requests vendors registered in the system to quote availability and a cost associated, payment method, and shipping options (124) to a preferred location of the said user. The real-time e-commerce system trained using deep learning models accurately identifies and classifies the objects, maps the object label set with its respective product category which in turn, creates product attributes to search vendors in the specific category in real-time.

The invention relates to a dynamic disease prediction system based on a real-time search trend and public data, belongs to the technical field of disease prediction, and solves the problems that during existing disease prediction, data acquisition is difficult, hysteresis exists, and a dynamic change relation between data is not considered, so that a prediction result is deviated. The system comprises a data acquisition module which is used for acquiring a public data set of continuous time with a city as a unit and performing preprocessing; the public data set comprises disease search indexes, meteorological data and social economic data; the dynamic disease analysis module is used for constructing and applying a multi-dimensional dynamic analysis model on the basis of the multi-dimensional public data set, and identifying and quantifying the data influencing the search index of the disease and the influence degree; wherein the multi-dimensional dynamic analysis model is used for capturing the dynamic relationship of the data and obtaining the time sequence relationship among the data; and the disease prediction module is used for predicting the disease development trend based on the data influencing the search index of the disease and the influence degree.

The application discloses an intelligent information interaction method and device, and relates to the technical field of Internet, and comprises the following steps: extracting preset content in voice or video recording information, adding a plurality of first label information after processing; performing intrasite real-time search on the first label information, and delivering feedback results to a digital person to perform information interaction; obtaining user information and real-time position information of a login user to continue adding a plurality of second label information, obtaining updated voice or video, and importing the updated voice or video into a content candidate pool; obtaining third label information of other login users, and judging whether the third label information is consistent with the first label information and the second label information; when the third label information is consistent with the first label information and the second label information, judging whether the updated voice or video in the content candidate pool is automatically pushed, if yes, pushing the updated voice or video in the content candidate pool to matched login users, if not, jumping to the first step when there is an input requirement; when the third label information is inconsistent with the first label information and the second label information, performing extracorporal real-time search.

The invention discloses a dynamic commodity recommendation method and system based on model multiplexing, and the method comprises the steps: obtaining behavior sequence data of a user in a preset event window, and extracting a feature code of the recent commodity preference of the user; and injecting a task specific adapter library based on the basic recommendation model, training the basic recommendation model by using the feature codes of the recent commodity preferences of the user, generating a new adapter, and storing the new adapter in the task specific adapter library. And selecting the adapter with the highest similarity as a historical adapter and storing the historical adapter into the task specific adapter library by utilizing the interest label vectors of the adapters in the task specific adapter library and the real-time search query word. And fusing the historical adapters and the new adapters to obtain recommended adapters, and loading the recommended adapters into the basic recommendation model to obtain a unified recommendation model. And using the unified recommendation model to predict and sort interested commodities of the user to generate a recommendation list. According to the method, recent data are quickly and finely adjusted through the lightweight adapter library, the short-term interest change of the user is sensitively captured and responded, and the recommendation result is more accurate.

The present invention relates to an online road matching method based on Beidou positioning, the method comprising: collecting Beidou positioning data of each positioning point in a vehicle trajectory in real time, filtering the positioning points based on preset filtering conditions to select qualified positioning points; for the qualified positioning points, using a geohash retrieval algorithm and a topological search combined method to complete a real-time search of a candidate road section set; scoring each candidate road section in the candidate road section set in real time, and outputting a real-time scoring result for each candidate road section; for the real-time scoring result of each candidate road section, selecting the optimal current matching road section and current matching point based on a preset result selection strategy, connecting the current matching point in series with the previous matching point to form a complete trajectory, and achieving real-time matching of online roads. The present invention also relates to an online road matching device, medium, and equipment based on Beidou positioning.

This invention discloses an adaptive reinforcement learning-driven multi-objective optimization method for accelerators, belonging to the technical field of accelerator optimization design. First, it analyzes the mechanism of action of key design variables such as convolution loop unrolling factor, quantization accuracy, and cache partitioning, establishes a system-level state modeling method, and designs the DDPG framework suitable for continuous action space optimization as a learner for the configuration strategy. Simultaneously, a lightweight searcher is implemented by running Python scripts on the CPU to search and allocate the block parameters of Loop-3 and Loop-4 in real time, thereby reducing the computational burden on the reinforcement learning agent. Finally, an adaptive configuration method driven by reinforcement learning and a corresponding search process are proposed to achieve joint optimization of unrolling scale, quantization accuracy, and block parameters.

The application discloses a kind of heterogeneous data caching methods for multimodal real-time search, it is related to heterogeneous data caching technical field, the application includes: S10: the real-time search feature of key information is constructed;S20: each shard is matched with each modality type;S30: the cache area of each cache object is clear;S40: the search index of each cache object in corresponding cache area is determined;S50: according to the search frequency of key information, the cache location of the search index corresponding to key information, first cache area and second cache area is determined.The application can combine cache information according to matching result, and then quickly obtain the first search result of key information, which is beneficial to the search system to quickly respond when facing different combinations of heterogeneous data, and update the real-time cache result of key information, and match the updated cache result with the modality type, which is beneficial to improve the search efficiency of the search system.

A search request received at a computer of a search support system is processed by analyzing the received search request to identify request parameters and connecting to a system index of the search support system that is referenced in the request parameters. An external result provider (ERP) process is initiated that establishes communication between the search support system and a data source external to the search support system, for a virtual index referenced in the request parameters. Thus, the ERP process provides an interface between the search support system and external data sources, such as by third parties. The ERP process can operate in a streaming mode (providing realtime search results with minimal processing) and / or a reporting mode (providing results with a greater delay and processing extent) and can switch between modes. The search request results are received from the connected system indexes and the referenced virtual indexes.

A system for real-time indexing and synchronization of relational databases with a search engine cluster. The system can retrieve datasets using NTILE queries, serialize the data, and load the data into the search engine index via a bulk API. Changes in the relational database can be captured using triggers and stored in a delta metadata table with table identifier, record identifier and type of change. Periodic scans of the delta metadata table can identify the changed records, which can then be retrieved and used to update the search engine index. The system can support parallel processing of data chunks and dynamic adaptation to new database fields without code changes. Users can search the indexed data through an interface that allows attribute-based filtering and hierarchical display of search results.

1. The name of the design product: Loran time signal monitoring receiver (enhanced). 2. The use of the design product: for real-time search, capture, tracking and locking of all-view multi-chain eLORAN signals, and real-time output of monitoring data. 3. The design points of the design product: in the shape of the product. 4. The picture or photo that best indicates the design points: front view.

The invention discloses a dynamic cache management method for optimizing real-time search performance, which relates to the technical field of dynamic cache management and comprises the following steps of: acquiring a search efficiency curve of real-time search when a dynamic cache contains resources with different storage contents; obtaining a memory occupation amount; setting fixed cache content in the dynamic cache based on the searched content; the method is used for solving the problems that in an existing dynamic cache management method, data which a user pays attention to cannot be reserved in a dynamic cache, and the data volume of the reserved data in the dynamic cache cannot be flexibly adjusted, so that when the user browses a large amount of data, the real-time search performance is affected due to the fact that the dynamic cache reserves more data, and the user experience is poor. Therefore, the problem that the search efficiency of the user is affected due to poor real-time search performance when the user returns to the data which the user pays attention to is solved.