331 results about "Geographic site" patented technology

The invention provides an advertisement recommendation method and system fused with user dynamic behavior modeling. The method comprises the following steps: acquiring a cross-device operation record of a user on multiple terminal devices, and synchronously acquiring a device unique identification code, high-frequency click area data and an operation timestamp; and constructing a space-time relation graph based on combined analysis of the equipment switching time interval and the click behavior, and generating a feature set reflecting the behavior correlation degree. The trajectory similarity is calculated at the edge calculation node through hardware acceleration, and migration correlation parameters are generated. And inputting the space-time relation graph into a behavior analysis model to extract coherent behavior characteristics, and forming a multi-dimensional scene portrait in combination with geographic position change and interface element distribution. And finally, through identifying a user cross-device interest migration mode, dynamically screening advertisement contents matched with the current scene, and realizing accurate pushing strategy optimization based on behavior continuity. According to the technical scheme provided by the invention, the accuracy of cross-terminal advertisement recommendation and the scene response speed are remarkably improved.

The invention relates to a multi-model space-time combination flood peak prediction method fusing physical constraints, which comprises the following steps of: acquiring static space data, dynamic time sequence data and boundary data of a research drainage basin, converting the static space data of the digital elevation model into a grid matrix, and calculating the dynamic time sequence data of the research drainage basin according to the grid matrix and the dynamic time sequence data of the research drainage basin; representing an elevation value of each geographic position, extracting gradient features by using gradient calculation according to the elevation values, and performing normalization processing on the gradient features to obtain normalized gradient features; a CNN-Bi-LSTM-Transform prediction model is constructed, the prediction model comprises a spatio-temporal data alignment module, a CNN convolutional network, a spatio-temporal feature splicing module, a bidirectional long and short term memory network and a Transform module, the prediction model is trained, and a loss function during training adopts a physical constraint loss function composed of mean square error loss and water conservation constraint terms and is used for flood peak prediction.

The invention belongs to the technical field of geographic information data processing, and discloses a graph attention-based road slope surface displacement time sequence prediction method, which comprises the following steps of: obtaining surface displacement time sequence data of a plurality of monitoring stations and associated environmental influence data, and constructing a weighted adjacency matrix based on geographic positions of the monitoring stations, defining an initial spatial topological relation; carrying out feature fusion on the displacement data and the environment data, and constructing an attribute-enhanced feature matrix; respectively inputting the weighted adjacency matrix and the feature matrix into a graph convolutional network and a graph attention network for parallel processing; extracting structured spatial features by the GCN through a fixed topological structure, and generating a first feature representation; the GAT adaptively allocates a dynamic weight by using an attention mechanism, extracts a non-uniform spatial dependency feature, and generates a second feature representation; and fusing the two feature representations, inputting a time sequence modeling module to analyze time dependence, and finally outputting a surface displacement prediction result at a future moment. According to the method, the accuracy of surface displacement prediction is remarkably improved.

The invention relates to an improved NSGA-III-based multi-target geographic site selection optimization method and system, belongs to the technical field of optimization in business management and resource planning, and particularly relates to resource allocation and site selection decision making by using a calculation model. The technology aims to solve the problems of slow scheme convergence, non-uniform solution set distribution and low calculation efficiency when multi-target site selection is carried out under limited resources. According to the scheme, geographic space data is preprocessed, and an initial population representing different site selection schemes is generated; carrying out optimization iteration by adopting an improved NSGA-III algorithm, and calculating a plurality of objective functions such as coverage rate, normalized population density and medical service accessibility; and performing individual selection and population updating by utilizing improved reference point generation and vertical distance measurement. The method is mainly used for improving the efficiency and effect of commercial and municipal decisions such as urban planning, medical resource configuration, energy station layout and logistics network optimization.

The invention provides a spatio-temporal index construction method, a track point query method, devices, equipment and a product, and the spatio-temporal index construction method comprises the steps: obtaining vehicle track point data, the vehicle track point data comprises the following attribute information of a vehicle track point: a track point identifier, a timestamp and a geographic position; according to the timestamps and the track point identifiers, a B-group tree index is constructed, index key information of leaf nodes in the B-group tree index comprises the timestamps and the track point identifiers, and the B-group tree index is a time index of the vehicle track points; and according to the geographic position and the track point identifier, an R-family tree index is constructed, the space entry information of the leaf node in the R-family tree index comprises the geographic position and the track point identifier, and the R-family tree index is the space index of the vehicle track point. According to the method and the device, the efficiency of querying the track points in the time dimension and / or the space dimension can be improved.

The invention relates to the technical field of electronic commerce, in particular to a personalized analysis method for cultural and creative products based on collaborative filtering, which comprises the following steps: acquiring browsing, collection and purchase behavior data of a user, calculating interval change of adjacent behaviors, and adjusting behavior influence based on browsing frequency, collection density and purchase conversion. And calculating a behavior contribution proportion in combination with the collection frequency and the stay duration, and extracting a short-term behavior trend to obtain a user behavior change trend parameter. According to the method, the behavior change rate and the time interval are combined for screening, the recommended content can quickly adapt to user interest changes, the recommendation matching degree is improved through utilization of social interaction data, social recommendation is more targeted, the scene applicability of the recommended content is enhanced through consideration of environmental factors such as geographic positions and weather, and the user experience is improved. The dynamic calculation of the multi-dimensional weight optimizes the recommendation sequence, and improves the long-term effectiveness and user experience of the recommendation system.

Described herein are techniques and mechanisms for geographic routing. A database system may store data records corresponding with user accounts and including historical routing information characterizing geographic routes determined in association with the user accounts. A communication interface may receive a request from a remote computing device authenticated to a user account and identifying an initial geographic location and a terminal geographic location. A geographic routing engine may determine a route including turn-by-turn instructions for moving from the initial geographic location to the terminal geographic location. A generative language model interface may complete a navigation prompt to include novel text identifying supplemental route information. A user interface generation interface may transmit an instruction to the remote computing device to present a user interface that includes the turn-by-turn instructions and some or all of the supplemental route information.

An online system receives a request to access an interactive geographical map of sources from a client device associated with a user. The system retrieves data describing a geographical location associated with the user. The system identifies one or more sources within a threshold distance of the location and retrieves data including information describing items available at each source. For each source, the system accesses and applies a machine-learning model to predict a user engagement score indicating a likelihood of one or more interactions by the user with a set of items available at the source if the source is included in the map. Based on the score for each source, the system selects a set of sources and generates the map, in which the map indicates the geographical location of each selected source. The map is then sent to the client device, causing the device to display the map.

Systems and methods are provided to select a set of candidate listings having geographic locations within a geographical area, fit a bound for a maps viewport to the set of candidate listings, generate a maximum score for the set of candidate listings based on a booking probability for each listing, a distance of the listing location from a center of the bound for the maps viewport, and visibility based on other listings. The systems and methods further optimize the listings shown in the maps viewport by removing and replacing farthest listings up to a predefined number of listings to be removed and computing a cumulative booking score for each set of listings to compare to the maximum score. The systems and methods cause display in a maps viewport in a user interface of a computing device of a set of listings that has a maximum score.

The invention is suitable for the technical field of privacy data processing, and provides a privacy grading and sharing control method for tourist travel itineraries, and the method comprises the steps: obtaining travel itinerary data of tourists; dividing the travel data into at least three basic privacy levels based on a preset region sensitivity classification rule, a user identity classification rule and an equipment type classification rule; in the basic privacy level, performing fine-grained sub-level division on the data according to interval division of travel duration, classification of activity risk levels and a historical sharing frequency threshold; based on the basic privacy level and the fine-grained sub-level, geographic position fuzzification and user identity anonymization processing are carried out on the travel data of the high-sensitivity level, and the access range of a shared object is limited through permission grading; and the sensitivity of the fine-grained sub-level is adjusted according to the real-time sharing frequency, and the sharing permission is updated, so that the accurate balance of the travel itinerary data between privacy protection and sharing effectiveness can be realized.

The invention discloses a user geographic position positioning method and device, terminal equipment and a storage medium, and the method comprises the steps: obtaining target social data of a social user, and the social data at least comprises target latitude and longitude and target user text data; according to a pre-established geographic position prediction model, geographic position information of a social user corresponding to the target social data is determined, and the pre-established geographic position prediction model is used for processing sample data, determining a multi-dimensional user relation graph corresponding to the sample data, and predicting the geographic position of the social user according to the multi-dimensional user relation graph. A multi-dimensional user relation graph is adopted to train a multi-scale feature fusion model based on a graph attention network, and a social network graph and a position association graph are constructed through the social relation of users and hidden position correlation; processing through the proposed multi-scale feature fusion model to obtain the overall representation of the user; the position of the user is deduced through the user positioning classifier, and the accuracy and efficiency of user position prediction are improved.

The invention relates to the technical field of data processing, and discloses an unmanned aerial vehicle photo and video management method and system based on a geographic position. The method comprises the following steps: extracting geographic attribute data from an unmanned aerial vehicle photo EXIF and video subtitles; establishing a spatial database, storing photos as a point graph layer, and storing videos as a line graph layer; renaming the file according to the shooting time and the camera model; importing the point-line graph layer into GIS software to generate a visual graph; searching and screening target area media through spatial analysis; and performing statistical analysis on the media set to generate a comprehensive report. According to the method, unmanned aerial vehicle photo and video data can be managed at the same time, geographic position information is fully utilized, multi-dimensional space retrieval is supported, and the method is suitable for various application scenes, so that the complexity and errors of manual arrangement are eliminated, and the management efficiency and retrieval precision of unmanned aerial vehicle media data are improved.

The invention discloses a traffic accident risk determination method and device, electronic equipment and a storage medium, and belongs to the technical field of intelligent traffic. In the method, a specified geographic area is divided into a plurality of grids, traffic data of the specified geographic area in past T time periods are acquired, and the risk of a traffic accident is determined based on the traffic data in the T time periods; the method comprises the steps of generating T parts of graph structure data and T parts of grid structure data, enabling any one piece of graph structure data to represent the dependency relationship of traffic accidents on geographic semantics, enabling any one piece of grid structure data to represent the dependency relationship of the traffic accidents on geographic positions, then analyzing the spatial-temporal correlation of the traffic accidents on geographic semantics based on the T parts of graph structure data, and obtaining the spatial-temporal correlation of the traffic accidents on geographic semantics. Based on the T grid structure data, the time-space relevance of the traffic accident in the geographic position is analyzed, and the traffic accident risk value of each grid in the (T + 1) th time period is determined by integrating the analysis results of the T grid structure data and the time-space relevance, so that a traffic accident prediction scheme is provided.

A computer implemented method includes: obtaining a plurality of predictive data, each predictive data indicative of respective predictive values of a characteristic corresponding to geographic locations at a worksite; obtaining in-situ data indicative of in-situ values of the characteristic corresponding to the geographic locations at the worksite; determining a respective accuracy of each predictive data based on the respective predictive values of the characteristic and the in-situ values of the characteristic; identifying, based on the respective accuracy of each predictive data, a set of qualified predictive data comprising two or more of the predictive data; determining whether a selection condition is present at the worksite; selecting one qualified predictive data, of the set of qualified predictive data, as selected qualified predictive data based on the determination of whether the selection condition is present at the worksite; and controlling a mobile work machine based on the selected qualified predictive data.

The invention provides a data compliance migration method which comprises the following steps: acquiring data protection regulation change information through a regulation monitoring module, extracting key terms and geographic position parameters, and generating a compliance parameter set; triggering data classification identification according to the regulation change information, analyzing content features and sensitive levels of stored data, and determining risk classification of data blocks; distributing the data blocks to different transmission queues according to the risk classification, and generating a data migration task list; dynamically scheduling a data migration time window and a transmission quantity according to the risk classification and the optimal migration path; starting a parallel data transmission process, and executing a data migration operation; carrying out integrity check on the transmission data through a check algorithm, and confirming a transmission state; and the monitoring module is used for detecting service response time in the migration process and adjusting the migration progress and the switching opportunity.

The invention discloses a post recommendation method and device, equipment, a storage medium and a program product. The method comprises the following steps: in response to a client login request of a user, obtaining real-time geographic position information of a job application client; screening out at least one first-class aging recruitment enterprise of which the regional information is matched with the real-time geographic position information in the aging recruitment order metadata set which is maintained in real time; filling the description information of each first-class aging recruitment enterprise into a recommendation page, and displaying the recommendation page; and in response to a check ending request of the user for the recommended page, normally opening a conventional function interface of the job application client to provide a job application service for the user. According to the technical scheme of the embodiment of the invention, before a conventional job hunting function is provided for the user, a batch of recruitment enterprises matched with the real-time geographic position information of the user can be screened out for advanced recommendation for the user, so that the workload of brokers is greatly reduced, and meanwhile, the in-job achievement rate of members can be effectively guaranteed.

In some embodiments, a method for training a machine learning device includes: ascertaining geographic location information of at least one portion of a first image associated with a label; associating with the label a second image including at least a portion having substantially the same geographic location information as the at least one portion of the first image; optional alignment or coregistration of the first and second image to maximize mutual information overlap; forming a training dataset comprising the first and second images as input images and the label that the first and second images are associated with as outputs; optional binary categorization and curation of the resulting training dataset to ensure accuracy; and training the machine learning model using the augmented dataset.

The invention discloses a low-efficiency land use identification method and device based on a pressure-state-response framework, and the method comprises the steps: obtaining the geographic position data, land use type data, social and economic data, population data, ecological environment data, infrastructure data and regulation and control response data of a plurality of target regions; calculating a spatial correlation coefficient between the first target area and the second target area based on the geographic position data, the land use type data, the social economic data and the ecological environment data to obtain a spatial correlation matrix; based on the social economic data, the population data, the ecological environment data, the infrastructure data, the regulation and control response data and the spatial incidence matrix, constructing a low-efficiency land use evaluation index system; determining a comprehensive evaluation index set of each target area through the low-efficiency land use evaluation index system and the land use type data; and determining the low-efficiency land use level of each target area based on the comprehensive evaluation index set, and obtaining a low-efficiency land use identification result. According to the invention, the accuracy of low-efficiency land use identification can be improved.

The invention relates to the technical field of robustness artificial intelligence, and discloses an intelligent monitoring method and system for the flow state of a gas pipeline, and the method comprises the following steps: carrying out the grid division of a pipeline according to a construction layout diagram of the gas pipeline, and carrying out the leakage danger grade marking of the gas pipeline in each grid unit; for local gas pipelines in the grid units with different danger levels, the intensities of the installed sensor assemblies are different; the sensor assembly is used for collecting gas in the gas pipeline and equipment operation data; sensor data are collected, processed and transmitted through data acquisition and transmission equipment; determining geographical location information of an accident site by using positioning equipment; geographic information data support and path planning are provided by means of a geographic information system device. The system can be suitable for various installation environments and is high in monitoring accuracy.

The invention discloses a cross-view geographic positioning method based on an attention mechanism, a medium and equipment. The method comprises the following steps: receiving image information shot by an unmanned aerial vehicle or a satellite; and inputting the image information into the trained neural network model, and outputting geographical location information corresponding to the image information. When the neural network model is trained, through modes of multi-dimensional feature fusion, deep convolution optimization, cross-view feature alignment and the like, robustness of attention to a target building or an important area during transformation of image offset, rotation and the like in a geographic positioning task is remarkably improved; therefore, the difficulty in positioning in complex scenes such as view angle change and geometric shape difference is overcome.

The invention discloses an output optimization method, system and device under complementary scheduling and a medium. The method comprises the following steps: acquiring data of cascade hydropower, a wind field and a photoelectric field; a graph neural network is constructed based on the geographic position data, the weight of an edge is configured according to historical power generation data, and nodes of the graph neural network represent fans, photovoltaic arrays and hydropower stations at different geographic positions; carrying out meteorological prediction according to the current short-term meteorological data based on the constructed graph neural network; and constructing an evaluation model based on the double-layer Monte Carlo-conditional risk, outputting a probability density function of output prediction according to the evaluation model, and executing output optimization. According to the method, the propagation condition of possible meteorological elements in the non-uniform observation network is captured by establishing the graph neural network; and comprehensive evaluation of water, wind and light is realized by constructing an evaluation model, and a reference direction of short-term complementary output optimization is given through a probability density function, so that the scheduling effect of complementary optimization is improved.

The invention discloses a sample sending process monitoring system fusing image recognition and geographical location information, comprising a multi-source information acquisition module for acquiring image information around a sample and geographical location information of the sample in a sample sending process; the sample sending abnormal behavior recognition module based on image recognition is used for detecting pedestrian abnormal behaviors, camera abnormal shielding and camera angle abnormal adjustment in a sample area according to the image information; the sample sending abnormal behavior recognition module based on the geographic position information is used for extracting space-time trajectory characteristics of the sample according to the geographic position information of the sample and detecting a time abnormal trajectory, a space abnormal trajectory and a space-time abnormal trajectory according to the space-time trajectory characteristics; and the early warning module is used for generating an abnormal behavior signal or a non-abnormal signal according to the detection result of the sample sending abnormal behavior and the abnormal track behavior, and sending early warning information according to the abnormal behavior signal. The method has the advantages of small algorithm size, easy migration, low cost and the like, and can improve the user experience.

The invention discloses a social relation chain value evaluation method and system based on multi-dimensional data modeling, and the method comprises the steps: integrating a social platform API, a user behavior log, geographic position data and equipment information through a data collection module, carrying out the cleaning and standardization of a preprocessing module, dividing a geographic region through a space-time modeling module based on K-means clustering, and carrying out the calculation of the value of a social relation chain. An exponential decay function is introduced to dynamically update an interaction weight, an emotion analysis module adopts a BERT model to carry out emotion classification on comments, a role classification module marks community roles through a rule engine, and finally a value calculation module fuses feature parameters of data of all dimensions to construct a propagation value evaluation model. According to the method and the system, the propagation value evaluation accuracy is remarkably improved, the misjudgment rate is greatly reduced, the social e-commerce propagator screening efficiency, the local life service precise popularization coverage rate and the brand propagation positive word-of-mouth recognition accuracy are improved, and scientific decision support is provided for social fission marketing.

The invention discloses a dynamic distributed scanning method and system based on an IPv6 geographic tag, and relates to the technical field of the Internet, and the method comprises the steps: carrying out the region division of an IPv6 seed address through combining with geographic information, obtaining the IPv6 seed addresses of a plurality of geographic regions, constructing an address space tree of each geographic region, determining the change dimension of the address space tree, and carrying out the calculation of the change dimension of the address space tree; generating candidate sub-modes based on the change dimension, and selecting a sub-mode from the candidate sub-modes; generating an address according to the sub-mode, and further obtaining a target address; performing comprehensive evaluation on each sub-mode region based on the seed address density and the network development condition to obtain an initial score, and scanning a target address in each sub-mode region according to the initial score; in the scanning process, the initial score is updated by adopting a score updating mechanism, and scanning is continued based on the updated score. Geographic positions and mechanisms based on IPv6 seed addresses are fused, so that the number of real active addresses is hit to the maximum extent under a limited scanning budget, and the detection efficiency is improved.

The invention provides a method and system for improving the timeliness rate of sample inspection, and relates to the technical field of sample processing. The method comprises the steps of collecting an identity number and a real-time task node number, recording a log after comparison succeeds, recording deviation and generating a compliance identifier if comparison fails, checking a space permission according to the identifier and updating a handover permission, if a specimen is in a warehouse-out state and the permission is effective, updating the specimen to be in transportation and generating a signature chain entry, and sending the signature chain entry to a server; and if the path is abnormal, locking the transportation state and re-planning the path, and generating a handover trigger instruction. According to the invention, through real-time matching of the identity of the collector and the task node and combination of code scanning time recording, accurate control of the operation process is realized, human errors in the specimen processing process are avoided, the concept of spatial authority and the beacon technology are introduced to ensure the accuracy of the geographic position of specimen handover, and the security and traceability are enhanced. The response speed and the processing flexibility are also improved, and the timeliness and the accuracy of specimen inspection are remarkably improved.

The invention discloses an intelligent two-dimensional code inspection method, an intelligent two-dimensional code inspection device and intelligent two-dimensional code inspection equipment for a survey mark, and a computer readable storage medium. The method comprises the following steps: generating two-dimensional code image data based on acquired benchmark information; wherein the benchmark information comprises a serial number, a roll call, a type, a grade, a geographic position, a supervision unit, a maintenance unit and a supervision telephone; scanning a two-dimensional code mark arranged on the top surface of the corresponding benchmark protection disc through a mobile terminal to obtain two-dimensional code image data; wherein the two-dimensional code mark corresponds to the two-dimensional code image data; obtaining the benchmark information according to the two-dimensional code image data, and displaying the benchmark information based on a page format; acquiring input inspection data through the mobile terminal; and performing inspection early warning processing based on the inspection data. The inspection efficiency of the survey mark is improved, and the labor cost is reduced.

The invention provides a meter reading path planning method and system based on multi-target constraint, and relates to the technical field of meter reading path planning, and the method comprises the steps: obtaining a to-be-read meter task set, and extracting historical state data of a geographic position and an associated metering device as feature data; predicting the predicted operation duration of each meter reading task according to the feature data; performing spatial clustering division on the task set to be subjected to meter reading through an improved watershed algorithm to form a plurality of task clusters; taking minimization of the total driving distance and the total prediction operation duration as optimization targets, performing initial path planning, and generating an initial operation sequence of each task cluster; and executing the initial job sequence, calculating the deviation between the actual job duration and the predicted job duration, and re-performing path planning on the meter reading tasks which are not executed subsequently to generate an updated job sequence. The technical problems of task allocation imbalance, path static solidification and poor multi-target collaboration in meter reading path planning in the prior art are solved.

The invention discloses a remote sensing image retrieval method and device, electronic equipment and a medium. The method comprises the steps that a remote sensing image retrieval request is acquired; analyzing the retrieval request through the target large model to obtain an initial retrieval condition; according to the initial retrieval condition and a pre-constructed interest point database, determining a target geo-fence retrieved by the current image; and based on the initial retrieval condition and the target geo-fence, performing retrieval in a remote sensing image database to obtain a target remote sensing image. Therefore, retrieval request analysis of complex geographic description and fuzzy text description is realized through the target large model, so that the remote sensing image retrieval intention of a user is accurately understood, and meanwhile, an accurate target geographic fence is obtained in combination with the pre-constructed interest point database, remote sensing retrieval is locked in a more accurate geographic position range, and the remote sensing retrieval accuracy is improved. Therefore, the retrieval precision of the target remote sensing image is improved.

The invention provides an enterprise intelligent early warning method based on an artificial intelligence model, and the method comprises the steps: extracting the spatial-temporal trajectory features of a target product in combination with a circulation data set, recognizing a circulation time interval, carrying out the mapping of geographic position information and the time interval through a geographic information system, and generating a spatial-temporal feature matrix of product circulation; grading the abnormal product circulation by combining the abnormal geographic position deviation value, the dispersion index corresponding to the dispersion degree, the product batch scale, the arrival time and the network site scale; according to different levels of abnormal risks, sending an early warning signal to a management platform, and determining early warning triggering time and an object; according to the early warning signal triggering condition, generating an abnormal intervention adjustment scheme, monitoring a product circulation track, and obtaining track change data after intervention; and updating the spatial-temporal characteristic matrix according to the trajectory change data, and re-inputting the data into the deviation detection and dispersion degree analysis process through a cyclic feedback mechanism to obtain a new anomaly recognition result.

The invention relates to the technical field of broadcast signal dynamic switching, and discloses a multi-mode broadcast signal intelligent switching method and system based on DRM, and the method comprises the steps: obtaining real-time geographic position information, real-time weather conditions and a historical switching record database; inputting the real-time geographic position information and a regional transition zone obtained by regional analysis into a geographic region division model to obtain the number of DRM broadcast signal modes; inputting a dynamic weather weighted data set constructed according to real-time weather conditions into the signal attenuation model to obtain a real-time signal intensity attenuation granularity value; extracting similar historical switching sequences according to the historical switching record database; performing matching calculation according to the similar historical switching sequence to obtain a matching result confidence coefficient; according to the similar historical switching sequence and the DRM broadcast signal mode number, multi-mode broadcast signal switching planning analysis is carried out, and a real-time optimal switching sequence is obtained. According to the method, high-precision matching of the historical switching data and the real-time scene can be realized.