97 results about "Geographic site" patented technology

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 application discloses a kind of based on multi-source data fusion's catering intelligent decision-making method and system, it belongs to site selection decision-making technical field, by using big language model to carry out knowledge extraction to multi-source data, and to this construction catering site selection knowledge graph, based on knowledge graph automatically calculates geographic position index, traffic accessibility index, consumption potential index and the objective weight of competitive environment index, simultaneously using big language model to the qualitative description of the region to be selected is retrieved to obtain membership vector, according to objective weight and membership vector calculation comprehensive suitability degree score, the recommended site selection region is obtained by ordering comprehensive suitability degree score, it quantifies index weight by catering site selection knowledge graph, avoids subjective dependence on expert experience, quantifies index using big language model, significantly improves the accuracy and efficiency of catering site selection, adapts to large-scale multi-source heterogeneous data scene, and in the face of dynamic factors can respond to update in time, convenient for application and promotion.

The invention provides a multi-mode sensing inspection method based on terminal equipment. The multi-mode sensing inspection method comprises the following steps: collecting video data, audio data, geographic position data and IMU (Inertial Measurement Unit) data; video frames are extracted based on language trigger thinning, pose trigger thinning and visual trigger thinning; and finally, identifying hidden dangers based on the extracted video frames. According to the method, significant thinning processing of the video frames is realized through various kinds of perception information, the input load of the large model is greatly reduced on the premise that key semantic information is not missed, the problems of frame input limitation and low analysis efficiency of the multi-mode large model in analysis of the long-time industrial inspection video are solved, and the method is suitable for large-scale industrial inspection. Therefore, the efficiency and accuracy of large model analysis are improved. And the frame extraction process does not depend on a fixed frame rate, but is based on semantic driving of human behaviors and equipment states, so that the extracted key frame is more effective.

The invention discloses a similar event judgment method and device based on geographic position information conversion and a medium, belongs to the technical field of computer software, and aims to solve the technical problem of how to realize flexible and accurate similar event judgment and meet the requirements of different service scenes. According to the technical scheme, one or more judgment modes of geographic position conversion coordinate comparison, geographic position coordinate direct comparison, keyword comparison and completely equal comparison are selected, weight scores are configured for the selected judgment modes respectively, and the weight scores are used for indicating the importance degree of different judgment modes in final similar event judgment; and comprehensively analyzing the selected judgment mode, judging whether the events are similar events or not through weighted scores according to the configured geographical location distance and threshold value, feeding back the judgment result of the similar events to a user, and carrying out batch operation on the events by the user according to the feedback result, and checking the details of the events in detail for further processing.

The present disclosure relates to the technical fields of intelligent mines, driverless driving technologies, and maps. Disclosed are an agent interaction scenario construction method and apparatus, and a behavior prediction model training method and apparatus. The agent interaction scenario construction method comprises: acquiring sensor data of a plurality of agents and map data of geographic locations corresponding to the sensor data; for the sensor data of each agent, performing trajectory detection on the detected other agents represented by the sensor data of the agent to obtain a first trajectory; determining a second trajectory of the agent on the basis of the sensor data; on the basis of a scenario data admission condition corresponding to a preset scenario, screening the first trajectory and / or the second trajectory to obtain a target trajectory; and merging the map data and the target trajectory to obtain merged scenario data. The present disclosure can improve the efficiency and accuracy of constructing agent interaction scenarios.

The invention belongs to the technical field of computer vision, and particularly relates to a street scene ground object real-time semantic segmentation and geographic positioning method and system based on a camera, and the method comprises the steps: S1, carrying out the image collection and preprocessing; s2, performing semantic segmentation; s3, panoramic monocular depth estimation and scale recovery are carried out; s4, target detection and pixel extraction are carried out; step S5, resolving a space coordinate; and S6, performing Kalman filtering fusion based on a time sequence, and outputting a final convergence coordinate of a filter as a final geographic position of the ground feature. Different from a vehicle positioning technology which depends on a prior high-precision map for matching, the method provided by the invention has the advantages that a panoramic depth estimation and space coordinate resolving algorithm is utilized, the three-dimensional space position of a ground object can be directly deduced reversely from two-dimensional image pixels under the condition of no prior map data, and the WGS84 absolute geographic coordinate of the ground object is calculated in real time in combination with GNSS / IMU data.

The invention provides an electronic map display method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring view field information in an electronic map application, and determining a target geographic position based on the view field information; acquiring a real-time environment image collected at the target geographic position, and determining real-time meteorological information corresponding to the target geographic position based on the real-time environment image and the weather forecast data; determining meteorological display elements according to the real-time meteorological information and the real-time environment image; and determining a plurality of base map elements based on the field-of-view information, and rendering and displaying an electronic map interface corresponding to the field-of-view information according to the meteorological display element and the plurality of base map elements. According to the embodiment of the invention, the accuracy of displaying the weather information in the electronic map can be improved.

The application discloses a next interest point recommendation method based on a multi-active area perception network. The method fully utilizes the spatial aggregation of user behavior trajectories, filters the active area center of the user by analyzing the historical activity trajectory of the user, and divides and aggregates according to the distance to realize the acquisition of long-term stable user preference features in the active area. At the same time, the method is based on a negative sample sampling method of the neighbor area, filters the interest point through the activity area of the user, fully utilizes the geographical location characteristics, and acquires sample information with more rich information. The application is based on historical check-in data and other multi-modal information of the user, constructs a network model through a deep learning method, extracts user preference features to predict the next most possible place visited by the current user, has the characteristics of high accuracy and strong scalability, can timely grasp the user behavior trend, and provides guidance for user behavior trajectory prediction personnel.

This invention belongs to the field of UAV autonomous positioning and navigation technology, and relates to a UAV map construction method for environments where satellite imagery is unavailable. Specifically, it provides a method for constructing a UAV work area map based on aerial imagery, addressing issues such as image distortion and edge misalignment in existing area map construction methods. This invention orthogonally divides the UAV's aerial photography work area into a grid according to the same geographic coordinate system. The UAV performs a coverage flight mission within the work area according to a planned flight path, maintaining an interval of 50% of the field of view width between adjacent flight paths. During aerial photography, the UAV simultaneously acquires image and geographic positioning information (GPS information and yaw angle information of the center point) as image location points. Based on UAV sensor data and the UAV's flight trajectory and pose, keyframes of geographic location information are extracted from the aerial video, and then used to fill the corresponding grid content to obtain a global map of the work area.

The present application relates to the technical field of underground pipe network safety monitoring and artificial intelligence image processing, and discloses a third-party construction intelligent discovery and closed-loop supervision method and system, comprising the following steps: firstly, coordinate mapping is performed on multi-source video images to obtain geographic location information; a pre-trained industry large model is used to perform double-flow analysis, identify static construction elements and dynamic operation behaviors, and determine suspected construction events in combination with time sequence characteristics; then, based on the spatial distance of the construction center point and the pipe network and the pipe property, the risk level is calculated, and a differentiated graded early warning strategy is executed; finally, the on-site disposal feedback result is received, and the weight parameters of the industry large model and the determination threshold in the risk rules are dynamically updated. Through multi-modal feature fusion and self-adaptive closed-loop mechanism, the present application effectively solves the problems of high construction identification false alarm rate and supervision lag in complex scenes, and significantly improves the intelligent level of safety protection of energy pipe networks.

A system and a method continuously receives data at a first aircraft from a second aircraft while the first and second aircrafts are traveling. The data includes at least some positional information of the second aircraft. One or more anomalies in the data are identified that are indicative of one or more geographic locations where a global positioning system of the second aircraft was compromised. A digital map is automatically created that indicates the one or more geographic locations associated with the one or more anomalies that are identified in the data.

The invention relates to a method for indicating a geographic location to a user. The technical result consists in simplifying the process of providing mapping information. The proposed computer-implemented method comprises: recognizing, within textual content, an address expression beginning with at least one special character; generating a geo-link based on the recognized address expression; highlighting the recognized address expression during display of the textual content; launching a mapping information display application through the geo-link in response to selection of the highlighted address expression; obtaining the address expression through the geo-link; transmitting the address expression to a geocoding service; receiving geographic latitude and longitude coordinates corresponding to the address expression from the geocoding service; and displaying on a map a geographic location corresponding to the received coordinates.

This invention relates to a method for predicting urban surface temperature by combining LSTM and geographic information, belonging to the field of spatiotemporal modeling technology for urban surface temperature. It includes the following steps: S1: Acquire temporal LST and geospatial feature data to construct a spatiotemporal sequence input; S2: Based on geospatial features, generate the initial hidden state and cell state of a Long Short-Term Memory (LSTM) network through linear mapping; S3: Introduce spatial weights based on spatial distance to spatially weight and adjust the hidden state of the LSTM, modeling spatial heterogeneity; S4: Employ a spatial cross-attention mechanism to model the correlation between geographic location information and the hidden state, fuse spatial information, and output the prediction result. By sequentially introducing the above mechanisms, this invention achieves collaborative modeling of the temporal dependence and spatial non-stationarity of surface temperature without interfering with the LSTM temporal modeling process, significantly improving prediction accuracy and stability, especially in complex urban environments and cross-regional applications.

This invention discloses an output traffic prediction method, apparatus, and storage medium, comprising: acquiring historical traffic sequences and geographical location information of base stations; determining at least one neighboring base station with the closest temporal and spatial dimensions as the neighbor subgraph of the target base station; obtaining the original traffic sequence and discrete traffic sequences through threshold setting rules; acquiring spatial dependencies between each base station in the neighbor subgraph and the target base station based on traffic similarity or oppositeity through a graph attention network; acquiring temporal dependencies between the original traffic sequence and the discrete traffic sequence through a TCN; and outputting traffic prediction results based on spatial and temporal dependencies through a soft attention mechanism. This invention avoids learning excessive redundant information, improves the accuracy of network traffic overload prediction, and enables the discovery of deeper spatial dependencies. It also significantly improves the accuracy and computational speed of multi-step prediction.

The present invention provides a tour guide assistance system includes a user terminal, a positioning module, a points—of—interest (POI) database, a context—aware module, an augmented reality (AR) processing module, and a personalized recommendation engine. The system operates by acquiring a user's real—time geographical location and ambient environmental data via the positioning and context-aware modules. The AR processing module synthesizes this data to retrieve and generate highly relevant AR tour content from the POI database, which is then overlaid onto the live feed of the user terminal. The personalized recommendation engine tailors the depth, theme, and presentation style of the content based on a dynamically updated user profile. This invention addresses the limitations of conventional guide systems, such as static content, lack of environmental context, and poor personalization, by delivering an immersive and adaptive touring experience.

The invention discloses a visual marking method and equipment based on fusion positioning, and a medium, belongs to the technical field of airport scene monitoring and management, and aims to solve the problems that the recognition accuracy is reduced, the high computing power requirement is needed, the real-time monitoring adaptability is poor, and the recognition efficiency is high due to the fact that existing airport scene monitoring is easily shielded by vision. And accurate geographical location information is difficult to obtain. The method comprises the following steps: performing coverage calculation of a maximum monitoring area on a visible area on an airport map to obtain a polygonal visible area; acquiring positioning terminal signals of key equipment and workers in an airport range; carrying out conversion processing of related camera point locations on a real-time geographic coordinate system in the real-time position information to obtain a camera coordinate system; performing conversion processing on the camera coordinate system to obtain a two-dimensional image coordinate system; mapping the two-dimensional image coordinate system into a polygonal visible area; and performing shielding labeling display on part of shielding targets in the initial labeling display information to obtain fused labeling display information.

Techniques for generating a prompt for a language model to determine an action responsive to a user input, are described. In some embodiments, the system receives a user input, determines one or more application programming interfaces (APIs) configured to perform actions that are relevant to the user input and exemplars representing examples of using the APIs with respect to user inputs similar to the current user input. The system further determines device states of devices that are determined to be related to the user input and also determines other contextual information (e.g., weather information, time of day, geographic location, etc.). The system generates a prompt including the user input, the APIs, the exemplars, the device states, and the other contextual information. A language model processes the prompt to determine an action responsive to the user input and the system causes performance of the action.

This invention relates to a remote sensing image positioning method and system based on depth feature vector retrieval and matching, belonging to the field of remote sensing image positioning technology. The method includes: firstly, using a depth feature extraction model finely tuned from remote sensing image samples, extracting the first global feature vector of global orthophoto tiles, and constructing a global feature vector database through inverted index clustering; secondly, after preprocessing the remote sensing image to be located, extracting its second global feature vector, and obtaining the top K candidate tile images through nearest neighbor retrieval in the database; thirdly, performing coarse and fine local feature matching on the two, eliminating mismatched points using the RANSAC algorithm, and obtaining the reference tile image and corresponding point information; finally, calculating the affine transformation matrix based on the corresponding points to complete the mapping from pixel coordinates to the geographic coordinate system, outputting accurate geographic location information. This invention effectively improves the accuracy and efficiency of remote sensing image positioning and is suitable for various civilian application scenarios.

The invention discloses a community information retrieval method and system based on a large language model, a terminal and a storage medium, and the method comprises the steps: carrying out semantic analysis on query content through the large language model to obtain a geographic position name and a query semantic intention, and analyzing the geographic position name through a geographic space agent to obtain a query semantic intention; geographical location information is obtained; retrieving in a space-time knowledge base according to the geographical location information and a preset distance to obtain a community data candidate set; expanding the query semantic intention to obtain an enhanced query; performing conversion processing on candidate community data and enhanced query in the community data candidate set to obtain a community data semantic vector and a query semantic vector; and performing screening processing and statement refining processing on the community data candidate set according to the community data semantic vector and the query semantic vector to obtain a query answer. According to the method, accurate retrieval is performed according to the query intention of the user through the three layers of geography, atlas and vector modules, so that the requirements of the user are met.

Disclosed is a computer-implemented method for correlating multiple quantitative geological datasets obtained at one or more geographic locations. The method comprises: receiving multiple datasets including a first dataset and one or more second datasets, each dataset comprising a plurality of data points corresponding to measurements of a geological property of material obtained at different z coordinates at a respective geographic location; and aligning the multiple datasets on a common z scale by: determining a measure of correlation of the multiple datasets by fitting a single curve to the data points of the multiple datasets and calculating a deviation of the data points of the multiple datasets from the fitted curve; determining one or more separate z scale factors and z offsets to apply to each of the one or more second datasets based at least in part on the measure of correlation of the multiple datasets and corresponding predefined probability distributions for the z scale factors and z offsets for each second dataset; and modifying the one or more second datasets by applying each z scale factor and z offset to the corresponding second dataset. Also disclosed is a system for implementing the method.

The invention belongs to the technical field of meteorological prediction, and discloses an abnormal air temperature event prediction method, which realizes early recognition and early warning of an abnormal air temperature event by introducing a recurrent neural network and fusing historical air temperature observation data and geographical location information. The method comprises the following steps: firstly, automatically identifying and extracting an abnormal temperature event from large-scale meteorological observation data, and constructing a high-quality data set suitable for prediction modeling; secondly, performing dynamic feature extraction on the temperature time sequence by using a recurrent neural network, and capturing a time sequence evolution rule of the abnormal temperature; and finally, in combination with the geographic position and climate relevance, screening key places having relevance with the to-be-predicted place, establishing a spatial enhancement mechanism, and optimizing model input features. According to the method, effective fusion of the time dynamic features and the space correlation features is realized, and the accuracy of abnormal temperature event prediction and the model robustness are remarkably improved.

The invention discloses a space-time labeling and automatic analysis method and system for an automatic driving test problem, and relates to the technical field of automatic driving. The space-time marking and automatic analysis method comprises the following steps: synchronously collecting time sequence data of a multi-source perception sensor on an automatic driving vehicle; performing data fusion processing on the time sequence data to generate lane-level structured positioning data with time-space alignment; based on the structured positioning data, multi-modal feature extraction and hierarchical classification identification are carried out by using a deep learning model, and a classification result of the test problem is obtained; calling a high-precision map service, associating a classification result with geographic position information in the structured positioning data, and generating time-space annotation data; and performing visual rendering on the spatio-temporal annotation data on the high-precision map to generate a test problem map interface. Through the synergistic effect of three core technologies of multi-source data fusion, intelligent analysis and space-time visualization, full-process automation and intelligentization of discovery, positioning, attribution and presentation of test problems are realized.

System and methods for predicting future pest pressures are provided. A pest pressure prediction computing device includes a memory and a processor communicatively coupled to the memory. The processor is programmed to receive trap data for a plurality of pest traps in a geographic location, receive weather data for the geographic location, receive image data for the geographic location, identify at least one geospatial feature within or proximate to the geographic location, apply a machine learning algorithm to the trap data, the weather data, the image data, and the at least one identified geospatial feature to identify a correlation between pest pressure and the at least one geospatial feature, and generate predicted future pest pressures for the geographic location based at least on the identified correlation between pest pressure and the at least one geospatial feature.

This disclosure provides a method, apparatus, device, medium, and product for matching geographic sites. The method involves dividing the geographic site based on its location information to obtain a grid index; determining a candidate grid range around the site to be matched based on the grid index; the candidate grid range includes candidate matching sites corresponding to the site to be matched; determining the geographic location difference between each candidate matching site and the site to be matched, and determining candidate matching sites that meet geographic distance conditions based on the geographic location difference to obtain a target matching site; determining the spherical distance between the target matching site and the site to be matched to obtain a target geographic distance; and identifying the target matching site corresponding to the target geographic distance that is less than a preset spherical distance threshold as a successfully matched site of the site to be matched.

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.

Systems and methods for managing access to data based on a geographic location requirement are disclosed herein. According to an aspect, a system includes a first computing device comprising a data storage manager configured to manage access to stored data based on one or more predetermined rule(s) that specifies geographic location requirement(s) for permissible access to the stored data. The manager is also configured to receive, from a second computing device, a request to access the data. Further, the manager is configured to determine a geographic location of the second computing device, determine whether the geographic location of the second computing device meets the geographic location requirement(s), and permit or deny access to the stored data by the second computing device in response to determining that the geographic location of the second computing device meets the geographic location requirement(s). Furthermore, the geographic location of the client is dynamically tracked to ensure ongoing compliance with the location rule(s), and if at any point the client becomes non-compliant, access to the data is denied and the data stored on the client is deleted by the server.

The invention discloses an online car-hailing driver position caching method, which comprises the following steps of: preprocessing a position message reported by a driver, and mapping the preprocessed position message to a space-time key consisting of a geographic unit identifier and a time bucket number; according to a comparison result of the offset of the original timestamp in the corresponding time bucket and a preset threshold value, writing the driver identifier into a cache position identified by one or more continuous space-time keys, and setting uniform survival time for the cache position; in response to a nearby driver query request of the passenger, determining an associated geographic unit set according to a query position, and determining a target time bucket number according to a query moment; and obtaining and returning a nearby driver identifier set by concurrently reading a cache position jointly determined by the geographic unit set and the target time bucket number. A high-performance and extensible solution is provided for systems needing real-time geographic position management, such as online car-hailing and instant distribution.

This invention discloses a system and method for generating and providing intelligent departure time reminders on an electronic device. In one aspect, the method includes: automatically, without human intervention, identifying a calendar entry including a first identifier (e.g., text in a location-related header of the calendar entry, such as "Going to the dentist"), the first identifier corresponding to an unknown geographic location. The method also includes automatically, without human intervention, retrieving a previously visited address (e.g., a street address or GPS coordinates of a street address) associated with the first identifier. The method further includes: automatically, without human intervention, determining a departure time for the calendar entry based on the previously visited address (e.g., based on a route from the device's current location to the previously visited address). The method also includes: automatically, without human intervention, associating the determined departure time with the calendar entry.

The invention relates to a method (100) for operating a fleet system (50), comprising at least one vehicle (10) and at least one server (20), the method (100) comprising: - Provision (110) of a map data set by the server (20), wherein the map data set includes at least one positional information and wherein the positional information includes at least one geographic position and a country marker, wherein the country marker is characteristic of the country in which the at least one geographic position is located, - Filtering (120) the map data set depending on a negative list, - Providing (130) a filtered map dataset, wherein the filtered map dataset includes only location-related information to which a country marker is assigned that is not covered by the negative list and - Operating (140) at least one vehicle system of the vehicle (10) using the filtered map data set.

To provide a system and method for generating a real-time digital map indicating geographic locations where a global positioning system of an aircraft is predicted to be compromised and / or malfunctioning.SOLUTION: A system and method continuously receive data in a first aircraft (402) from a second aircraft while the first aircraft (402) and the second aircraft are in motion. The data includes position information for at least some of the second aircraft. One or more anomalies in the data are identified that indicate one or more geographic locations (504) at which a global positioning system of the second aircraft has been compromised. A digital map (500) is automatically generated that indicates one or more geographic locations (504) associated with the one or more anomalies identified in the data.SELECTED DRAWING: Figure 5