85 results about "Crowdsourcing" patented technology

The application relates to a semantic map updating method, device and computer storage medium. The method comprises the following steps: when a corresponding semantic map exists in an updating collection area, determining a first point cloud object of a crowd-sourced map newly collected by the collection area and corresponding first semantic information; when a matching point cloud object exists in the semantic map and matches the first point cloud object, obtaining second semantic information of the matching point cloud object; performing semantic average processing on the first semantic information and the second semantic information to obtain average semantic information of an updated point cloud object; and updating the semantic map according to the average semantic information. The method can improve the map updating precision.

Techniques for augmenting transparency of published transaction data are described. The techniques can include one or more of associating a financial account with an account of a user at a transaction management platform, receiving transaction data from the financial account, categorizing the transaction data, publishing the transaction data, assigning permission levels to user for different levels of access to the amount and granularity of published transaction data, storing an immutable record of transaction data from which changes to the transaction data can be determined, and enabling crowd intelligence about the entity, account, or transactions by recording interaction of users of the published transaction data.

A distributed computer system comprises one or more patient system, configured to be operated by patients seeking provider services, a front-end web server to interface to the one or more patient system, a back-end server system coupled to the front-end web server to receive patient information, a healthcare provider system that provides information about a procedure needed by a patient unable to pay for the procedure, providing the information to the back-end server system, and a donor computer system, for accepting and receiving messages from the back-end server system about funding patient procedures.

In a wireless communication system, at least one UE detects GNSS signal spoofing / jamming and reports a detected spoofing / jamming attack to a server in the network apparatuses using an extended LPP GNSS error reporting format with an additional error cause and an attack probability metric. Further, the network apparatuses may take an implementation-dependent decision depending on crowd-sourced information of one or more spoofing / jamming attack reports received from one or more UEs.

The application provides a meteorological anchor point-based cross-modal asynchronous data alignment method, system, device and storage medium, which comprises the following steps: acquiring multi-modal data of a target area, and performing spatial alignment on the multi-modal data; constructing a unified anchor point time axis based on the meteorological predicted water depth grid sequence; based on the unified anchor point time axis, performing nearest time point matching and time decay on a remote sensing grid observation sequence and a GIS risk grid observation sequence respectively to obtain a time-aligned remote sensing grid observation sequence and a time-aligned GIS risk grid observation sequence; based on the unified anchor point time axis, performing time window aggregation on a social crowdsourcing point observation sequence to obtain a time-aligned social crowdsourcing point observation sequence; generating metadata of each modal data for each anchor point timestamp on the unified anchor point time axis, and outputting the time-aligned multi-modal data and the metadata.

The application provides a high-precision map multi-source data fusion updating method and system, and the method comprises the following steps: acquiring professional acquisition master database data and crowdsourcing acquisition data; respectively performing collection processing on the master database data and the crowdsourcing acquisition data to acquire road network topology models of roads, lanes and ground objects; constructing road section association relationships of the master database data and the crowdsourcing acquisition data based on the road network topology models of the master database data and the crowdsourcing acquisition data; and constructing lane line association relationships of the master database data and the crowdsourcing acquisition data based on the road section association relationships, and performing fusion updating on high-precision map data in the master database. The application realizes automatic and rapid fusion updating of high-precision map lane line elements based on the combination of the professional acquisition master database data and the crowdsourcing acquisition data.

This invention discloses a hierarchical public data visualization retrieval system and method based on crowdsourcing analysis and data standardization. The system includes: a crowdsourcing analysis platform for receiving and reviewing feature indicators extracted from public data by expert users; a data standardization module for performing unified exchange rate conversion and reporting period standardization on financial data to ensure data comparability; an indicator calculation module for calculating individual development indicators according to specific formulas; an index generation module for generating individual development indices, regional development indices, and global development indices based on benchmark values, and generating macro-trend charts; a multi-level index database for storing multi-level data and external links; an interactive response module for responding to chart click events and drilling down to obtain detailed data; a data display module for generating the next level of charts; and a context-aware navigation module for automatically navigating to preset external information sources based on user-selected function tags and entity clicks in the charts. This invention lowers the barrier to data utilization through crowdsourcing, ensures the comparability of global data through standardization, and achieves seamless and efficient retrieval and navigation from macro trends to micro information.

A system and method for optimized airline selection in air travel bookings provides a comprehensive solution for enhancing user experience in choosing airlines. This system integrates an Al-driven web platform, communicably coupled with a database, which analyzes crowdsourced consumer input on airline service experiences across multiple metrics. A user interface of the platform enables users to submit feedback and view a public dashboard of airline evaluations. The Al system processes this data to generate and update performance assessments for airlines, aiding users in making informed decisions. Additionally, the system facilitates the selection and booking of air travel, optimizing the process through up-to-date performance assessments and user preferences, including using a virtual assistant to help with the selection and booking process. The system is industry-agnostic and adaptable to various applications beyond air travel.

A vehicle includes an on-hoard satellite navigation device, a telematics control unit, a non-transitory computer readable medium, and a processor. The on-board satellite navigation device is in communication with a global positioning system unit to acquire real-time information regarding conditions near the vehicle's vicinity. The telematics control unit is in wireless communications to a cloud services or a vehicle network to upload and receive crowdsourced information regarding conditions near the vehicle's vicinity. The non-transitory computer readable medium stores predetermined information regarding conditions near the vehicle vicinity. The processor is programmed to output one or more complexity values related to the vehicle's vicinity during vehicle travel based on one or more of the real-time information, the crowdsourced information and the predetermined information.

The invention discloses a storage optimization method and system for crowdsourcing query of knowledge maps in the field of traditional Chinese medicine. The storage optimization method comprises the steps that traditional Chinese medicine RDF triple data is stored in an adjacency list form; according to different modes of known predicate unknown subjects / objects and known subject-called / called-object combinations in crowdsourcing query, an uncertain edge index based on a hash table and a certain edge index based on a B + tree are correspondingly constructed, and index entries point to an inverted linked list of a storage entity ID sequence; dynamic maintenance of an adjacency list and an index during data increment updating is supported; and for unknown nodes in the query graph, retrieving each edge candidate entity ID set by using a mixed index, and based on a preset crowdsourcing cost, calculating and returning the first K candidate nodes with the minimum comprehensive cost by using a Top-K matching algorithm based on a maximum heap. According to the method, the problems of low storage efficiency, index mismatching and poor updating expansibility of the traditional Chinese medicine knowledge graph in a crowdsourcing query scene are effectively solved, and efficient storage, quick query and low-cost crowdsourcing are realized.

The invention relates to the technical field of distributed computing, in particular to a crowdsourcing edge cloud task scheduling method and system based on a hypergraph, and the method comprises a pre-scheduling stage: when a new job is received, a dynamic hypergraph neural network is used for modeling the current resource state of a cluster, the modeling explicitly captures the high-order dynamic association between resources, and the new job is obtained; generating a resource state representation for determining an initial placement position of the new job; in the rescheduling stage, in response to cluster state changes, a unified layered resource hypergraph model is constructed, and the layered resource hypergraph model integrates discrete multi-layer scheduling constraints into one; and performing resource matching and conflict resolution based on the hierarchical resource hypergraph model to execute rescheduling of the job. The task scheduling mechanism not only can accurately perceive and model high-order dynamic association among resources so as to avoid sub-optimal initial placement, but also can unify and coordinate multi-level scheduling constraints so as to realize quick resolution of conflicts and efficient optimization during operation.

The embodiment of the invention relates to the technical field of federated learning and information security, in particular to a federated multi-target scheduling method and system for industrial control crowdsourcing testing, which is characterized in that sensitive data such as ability, experience and the like of a tester are only reserved at a local terminal by constructing a framework combining local feature coding and federated model training, so that the test efficiency is improved, and the test efficiency is improved. Outward transmission is not carried out; only the model update quantity after the privacy enhancement processing such as gradient cutting and differential privacy noise injection is uploaded to the federated matching learning server, and only the model update quantity after the privacy enhancement processing is uploaded to the federated learning server, so that the leakage risk of concentrated data storage is avoided from the source, the requirements of privacy regulations such as GDPR and the like are completely met, and the reliability of the system is improved. Compared with traditional privacy technologies such as homomorphic encryption, the method has the advantages that the calculation and communication overhead is greatly reduced, the design of local matching degree calculation is matched, the task matching response speed is greatly increased, and the real-time requirement of industrial control system testing is met.

An example method for updating global navigation satellite system (GNSS) error correction data for global navigation satellite system (GNSS)-based positioning. The method performed by a server may include obtaining a plurality of GNSS measurements crowdsourced from one or more GNSS devices across a plurality of measurement periods; determining a GNSS measurement residual using the plurality of GNSS measurements and existing GNSS error correction data; and transmitting updated GNSS error correction data for GNSS-based positioning, wherein the updated GNSS error correction data is determined using the determined GNSS measurement residual and the existing GNSS error correction data.

The application relates to the technical field of data labeling, and discloses a labeling method for model training data, which comprises the following steps: obtaining a multi-modal original data packet for model training; constructing a projection mapping matrix from a three-dimensional point cloud coordinate system to a two-dimensional image pixel coordinate system by using an internal parameter matrix of a camera acquisition device and an external parameter matrix of the camera acquisition device relative to a point cloud acquisition device; based on the projection mapping matrix, aligning an image frame in image data with a point cloud frame in point cloud data by using a timestamp sequence of the image data and the point cloud data, generating a spatio-temporal alignment fusion data frame; and labeling the fusion data frame. Based on the projection mapping matrix and the timestamp sequence of the image data and the point cloud data, the spatio-temporal alignment fusion data frame is generated, an objective geometric basis for data labeling is constructed, and the labeling accuracy of the labeled data is significantly improved. The application further discloses a labeling system for model training data, an electronic device and a crowdsourcing data labeling platform.

The application provides an indoor positioning method and system based on multi-modal data fusion of RMLICT. First, in the mapping stage, a fingerprint map is constructed from unbalanced crowd-sourced trajectory data. In the offline stage, trajectory data enhancement, dimension reduction and clustering processing are performed. In the online positioning stage, direction matching filtering is performed first, and then enhanced particle filtering is used to establish the mapping relationship between the RSS signal and single-step motion. This method optimizes data preprocessing and uses advanced particle filtering algorithms, which not only improves the positioning accuracy and robustness, but also significantly improves the real-time performance of the system, and is suitable for various complex indoor environments, such as scenes with severe multipath effect and signal obstruction.

With the widespread ubiquitous connectivity to smartphones and the Internet and social networks, software applications and cameras have become commonplace for individuals in our daily lives. We take pictures and videos of all kinds of events, goods and situations on our smartphones, easily upload them to cloud services, and with friends, family and others who subscribe or follow our shared content. However, through the use of all this technology we do not do a very job of recording the entirety of data available since other sensors are not utilized in the capturing of a significant, fleeting event, such as a UFO sighting, suspected criminal conduct, etc. Embodiments allow users to employ numerous sensors and other data to capture information about an event.

Systems and methods are described for determining an accuracy of crowd-sourced information. One or more servers can be configured to interact with and receive information from a plurality of devices / sources about an event, which may include, for example, sensors, social media platforms, and user inputs. The system can analyze information about the source and information about the event received from a source to determine the reliability and accuracy of the received information. This information can be cross-checked with social media posts, nearby sensor data, and other inputs to verify authenticity and accuracy. If an event is verified or deemed reliable, the system can autonomously decide that an alert and / or guidance can be presented or transmitted to one or more recipients.

Embodiments described herein relate to dynamic selection of radio access technologies (RATs) available for use by 5G wireless devices. Managing selection of RATs to use by a 5G (and 4G LTE) capable wireless device can improve performance, e.g., when an application requires a higher throughput and a 5G SA connection underperforms a 5G NSA connection or a 4G LTE connection. Wireless devices can provide geolocation tagged cellular wireless network information, e.g., network performance measurements and observed properties, to network-based servers that collate and organize the crowd-sourced cellular wireless network information into a location database. The wireless device can use the cellular wireless network information of the location database for a geolocation at which the wireless device is operating to estimate performance for alternate wireless connections and compare to measured performance of a currently used wireless connection to determine whether to switch wireless RAT connections.

The invention discloses a spatial crowdsourcing fuzzy task recommendation method and system based on differential privacy, and relates to the technical field of spatial crowdsourcing. Performing fuzzification processing by using a K-anonymity technology according to a task request of a task requester to obtain a fuzzy request and submitting the fuzzy request to a fog node; the fog node receives the fuzzy requests, aggregates the fuzzy requests by using a user-based collaborative filtering technology to obtain a group request, and sends the group request to the cloud server; the cloud server performs task recommendation on the group request by using a BERT2FTR model, and sends the group request to the fog node; and the fog node distributes the group result to the corresponding task requester. According to the method, a BERT2FTR model subjected to differential privacy training is introduced to design a fuzzy recommendation and privacy protection mechanism, fuzzy processing on task requests is realized by using a K-anonymity technology, and the task requests are aggregated by using a user-based collaborative filtering technology. And processing fuzzy task recommendation by using a BERT2FTR model to realize privacy protection.

This invention provides a deep learning-based crowdsourced database query optimization and trusted data processing system, relating to system software and database fields. It includes a query optimization engine module, a task allocation module, a data trust assurance module, and a trusted blockchain center module. The query optimization engine module generates an optimization execution plan and enables online model evolution through asynchronous incremental training triggered on demand by a verification submodule. The task allocation module achieves accurate task distribution through graph model task modeling and iterative matching. The data trust assurance module adopts an on-chain and off-chain collaborative architecture, detects data tampering through hash consistency comparison, and completes database recovery based on time-slice replicas and legitimate transaction replay. The trusted blockchain center module provides privacy computing and automatic verification and evidence storage services for multi-stream data. This invention effectively solves the problems of low query efficiency, inaccurate task matching, and data tamper-proofing and compliance self-verification in high-concurrency environments in crowdsourced database scenarios.

A method for detecting grouped traffic sign objects comprises receiving image data from a multitude of vehicles, wherein the image data captures a multitude of traffic signs, refining the image data by filtering it to remove unsuitable image frames, and assigning each traffic sign captured in the image data to one or more sign groups. For each corresponding sign group within the one or more sign groups, the method also comprises processing the corresponding sign group to extract metadata associated with each of the traffic signs assigned to the corresponding sign group, deriving a target for the corresponding sign group based on the metadata, deriving a context for each of the traffic signs assigned to the corresponding sign group based on the metadata, and storing the corresponding sign group in a data store.

The application relates to a method and device for updating a three-dimensional point cloud crowdsourcing semantic map. The method comprises the following steps: matching a newly collected crowdsourcing map and an existing semantic map of the same collection area to obtain at least one of a first unmatched point cloud object set and a second unmatched point cloud object set; judging whether each first unmatched point cloud object in the first unmatched point cloud object set collides with a corresponding point cloud object in a preset semantic distance range of the semantic map; judging whether each second unmatched point cloud object in the second unmatched point cloud object set collides with a corresponding point cloud object in a preset semantic distance range of the crowdsourcing map; and updating the semantic map according to the bounding box collision result of the first unmatched point cloud object and / or the bounding box collision result of the second unmatched point cloud object. The method can reduce object detection errors and improve the map updating accuracy.

The invention provides a mobile crowdsourcing trust evaluation method and system, and relates to the technical field of mobile crowdsourcing. Comprising the following steps: constructing a hypergraph according to user nodes, working nodes and crowdsourcing tasks; constructing an initial trust evaluation model by using a heterogeneous graph neural network; constructing an optimization loss function by using a cross entropy loss function and a dual-information bottleneck loss function; performing parameter optimization on the initial trust evaluation model by utilizing an optimization loss function and a gradient descent method to obtain a final trust evaluation model; and performing trust prediction on the hypergraph by using the final trust evaluation model to obtain trust prediction values of the user and the working node corresponding to the specific crowdsourcing task. According to the method and the device, the problems that an existing trust relationship prediction scheme based on a graph neural network cannot fully consider environment information and a condition trust relationship under a specific task, so that the scheme cannot adapt to dynamics and diversity in a mobile crowdsourcing task, and the trust prediction accuracy is influenced are solved.

The invention provides a real-time collaborative Web test capturing method and system based on multi-feature scoring, and relates to the technical field of Web automatic testing. According to the method, the interactive coordinates clicked by the user are obtained, cyclic penetration detection is carried out, and a candidate element sequence is output; then five-dimensional feature quantitative scoring is executed, a scoring result is output to select a target element, a corresponding unique identifier is generated, and a capture event is packaged; a change observer interface is used for monitoring change, and an anti-shake mechanism is combined to perceive the page steady state of the browser, so that when the page is structurally changed or redrawn, a related monitor is awakened, computing power is distributed, a target element is reselected, and a capture event is packaged, and when the page is stable, the capture event is sent to a back-end server. And broadcasting to other cooperation terminals in the same room is facilitated. According to the method, the problems of misalignment of element positioning and low cooperation efficiency can be effectively solved, and efficient real-time cooperation and financial-level data security protection of distributed crowdsourcing testing are realized while system resource loss is reduced.

The invention discloses a privacy protection task coverage and excitation method based on a double-cloud architecture in mobile crowdsourcing. The method aims at solving the problems of position information leakage, low high-difficulty task coverage rate, high excitation cost and the like in traditional crowdsourcing task allocation. A double-cloud server framework is constructed, privacy protection distance calculation between a worker and a task is achieved through a confusion circuit protocol, and leakage of position information of the worker and the task is avoided; meanwhile, a task coverage incentive mechanism based on a target gradient effect is designed, the workers are stimulated to continuously participate in the tasks by setting a gradually-propelled reward target, and the acceptability of the workers to the remote tasks difficult to cover is improved. The key technology of the invention is as follows: (1) a privacy protection distance calculation mechanism is provided, a real distance is jointly calculated in a double-cloud environment by disturbing and splitting position data, and both precision and privacy are considered; and (2) a nonlinear incentive mechanism based on behavioral economics is designed, so that the acceptance willingness of workers to high-cost tasks is remarkably improved, and a higher task coverage rate is realized on the premise of not remarkably increasing the incentive cost. Experimental results show that compared with an existing method, the method has the advantages that the task coverage rate is increased by 14.25% at most, and the excitation cost is reduced by 15.17% on average.

The application discloses a data labeling crowdsourcing quality improvement method, device and medium, the method comprises the following steps: determining the task demand of a data labeling task, determining the corresponding labeling personnel according to the task demand, issuing the data labeling task to the labeling personnel, so that the labeling personnel executes the data labeling task; collecting the labeling result of the data labeling task, determining the labeling result quantity according to the labeling result, and performing consistency verification on the labeling result quantity according to the pre-set verification standard; if the consistency verification is qualified, the labeling result is saved, and the labeling result is marked; if the consistency verification is not qualified, the data labeling task is reissued for execution until the consistency verification of the labeling result quantity is qualified. The process logic of the application is clear, the personnel is accurately matched from the task demand, the result is strictly verified, and the task is reissued if it is unqualified, so that the accuracy and high-quality output of the data labeling result are guaranteed.

The application provides a threat intelligence quality crowdsourcing evaluation method based on a reputation mechanism, and relates to the field of computer network security.The method provided by the application comprises the following steps: generating an objective score based on objective dimension evaluation by an automatic detection technology; obtaining an initial reputation value based on an additional reputation value and an initial reputation contribution value, and obtaining an initial subjective score of a subjective dimension by a crowdsourcing evaluator; screening out non-anomalous scores based on a weighted absolute median deviation, and obtaining a subjective score and a score entropy by post-hoc aggregation of the non-anomalous scores based on a weighted Dirichlet distribution model; obtaining a score deviation based on the initial subjective score and the subjective score, and constructing a reputation function to update the initial reputation value to obtain a reputation value; constructing a reward function based on the score deviation and the reputation value, and distributing rewards to the crowdsourcing evaluators; and obtaining a quality score by weighted fusion of each score.The application effectively realizes quality evaluation of threat intelligence by combining a crowdsourcing mechanism and a reputation reward mechanism.

Methods and system of building and augmenting a knowledge graph regarding ontology of events occurring in images. Image data corresponding to a plurality of scenes captured by one or more cameras is received. A knowledge graph is built with event-based ontology data corresponding to events occurring in the plurality of scenes. One or more of the scenes is displayed to a plurality of crowdsourcing workers which provide natural-language input including event-based semantic annotations corresponding to the scene. Using natural language processing on the input, triples are generated. The knowledge graph is augmented with the generated triples to yield an augmented knowledge graph for use in determining event-based ontology associated with the plurality of scenes.

The invention relates to the technical field of positioning and mapping related to intelligent transportation and automatic driving / vehicle infrastructure cooperation, in particular to a city-level crowd-source cooperation mapping and real-time positioning method and system, and the method and system achieve the stable fusion of city-level crowd-source data through standardized multi-source observation data, space-time reference construction and cross-source registration. And loop constraint and graph optimization are optimized through dynamic regulation and control parameters, so that the stability and the expandability under the conditions of heterogeneous sensors and data quality fluctuation are improved. By adopting multi-dimensional credibility characterization and a dynamic gating mechanism, loopback starting conditions are effectively controlled, the situation that a global map is polluted by wrong loopback caused by semantic anchor drift is avoided, the risk that structured errors are solidified into global consistency is reduced, and map safety is improved. Through a layered map and dynamic regulation and control, parameters can be optimized according to positioning residual errors and regional risk statistics during real-time positioning, and a closed-loop updating mechanism is formed.

The present application provides a travelable area planning method and an apparatus, used for reasoning the environment around a vehicle in combination with crowdsourced data, so as to plan a more accurate travelable area for the vehicle on the basis of limited obstacle information. The method comprises: acquiring crowdsourced device data and crowdsourced collection data, the crowdsourced device data comprising data of a plurality of second devices, the crowdsourced collection data comprising data collected by sensors in the plurality of second devices, and the second devices being also referred to as crowdsourced devices; performing data encoding, comprising encoding the crowdsourced device data to obtain device path encoding data, and encoding the crowdsourced collection data to obtain environmental semantic encoding data; and then inputting the device path encoding data and the environmental semantic encoding data into a neural network to output a first travelable area, the neural network being used for extracting the association relationship between the environmental semantic encoding data and the device path encoding data, and determining the first travelable area on the basis of the association relationship.