135 results about "Crowdsourcing" patented technology

The invention discloses a differential privacy-based multi-dimensional dynamic load balancing scheduling scheme (DP-DMAPSA), which is constructed on a DPDK framework and is oriented to high-concurrency scenes such as a security crowdsourcing platform and the like. According to the scheme, multi-dimensional indexes such as queue depth, CPU utilization rate and processing delay are introduced, differential privacy mechanisms such as Laplacian and Gaussian are combined, noise disturbance is performed on load data, and reasoning attacks are prevented. In scheduling, the system dynamically selects a processing core according to a weighted hash value and a privacy index to realize flow consistency and load balancing. Meanwhile, a self-adaptive sub-flow granularity adjustment mechanism is designed, dynamic adjustment can be carried out along with flow and load changes, and the scheduling precision and the resource utilization rate are improved. Experimental results show that the scheme improves the system throughput while guaranteeing the data privacy, and is suitable for scenes of security crowdsourcing, data centers, edge computing and the like.

The invention discloses a policy information crowdsourcing-oriented multi-level verification system and method, and relates to the technical field of data verification. A data source verification module; a content identification module; a distributed consensus verification module; and an abnormal behavior detection module. According to the policy information crowdsourcing-oriented multi-level verification system and method, the problem of data authenticity verification in government policy information crowdsourcing is solved, a four-level cascade protection framework is adopted as the core, and through four-level verification of data source verification, content identification, distributed consensus verification and abnormal behavior detection, the data authenticity verification efficiency is improved. The authenticity and compliance of the government policy information crowdsourcing data are ensured, the efficient, low-delay and anti-cheating system performance is achieved through algorithm optimization, and finally the core targets of 95% verification accuracy, 90% cheating detection rate and the like are achieved.

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.

The invention provides a confidence-guided adaptive graph representation reinforcement learning method, equipment and a medium, and relates to the technical field of label prediction and electric data processing. The method comprises the following steps: acquiring a crowdsourcing data set composed of multi-source heterogeneous electronic data, and inputting an adaptive graph representation learning model; mapping the crowdsourcing data set to construct an original bipartite graph; based on the original bipartite graph, generating a trimmed view and an enhanced view through dynamic confidence trimming and conservative edge addition; performing feature extraction, adaptive information enhancement and learning on the worker node and the task node through a dual-channel heterogeneous adaptive network, and performing prediction through a predictor to obtain an initial prediction label; and in combination with the original bipartite graph, performing label correction on the initial prediction label to obtain a final prediction label. The method can effectively suppress noise interference, capture local features and adapt to a dynamic environment, is remarkably superior to the prior art in the aspects of accuracy, robustness and calculation efficiency, and is suitable for the fields of multi-source heterogeneous intelligent labeling and the like.

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.

The present invention belongs to the technical field of high-precision map quality evaluation for autonomous driving. The present invention provides a crowdsourced map evaluation method and device based on lane lines, wherein the method includes: obtaining a sequence of lane line points based on crowdsourced data, using a custom distance calculation method between lane lines, hierarchically clustering the lane lines collected multiple times to obtain multiple clusters; for each cluster, performing polynomial fitting on the lane lines within the cluster, calculating the distance from each lane line to the fitted lane line, and mapping the distance to between 0 and 1 using a custom function to obtain the cluster-internal confidence; and calculating the global confidence of the map based on the confidence within each cluster and the total length of the lane lines. In the absence of a true map value, the present invention performs relevant calculations and processing on the lane lines to obtain the local and global confidence of the crowdsourced map, thereby respectively evaluating the local and global quality of the crowdsourced map.

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.

1. Name of the product of this design: A graphical user interface for displaying talent information on a cost crowdsourcing platform for electronic devices. 2. Purpose of this design product: an electronic device. 3. The key design point of this design product lies in the interface content of the graphical user interface. 4. The picture or photo that best illustrates the design points: Interface change state diagram 4. 5. Other designs are conventional designs and other views are omitted. 6. Purpose of the graphical user interface: The graphical user interface of this design product is used to display talent information on the cost crowdsourcing platform. 7. Description of the changing states of the graphical user interface: The main view is the initial interface of the program. Pull down the main view to interface change state diagram 1; pull down interface change state diagram 1 to interface change state diagram 2; pull down interface change state diagram 2 to interface change state diagram 3; click the person's name in interface change state diagram 3 to jump to interface change state diagram 4; pull down interface change state diagram 4 to interface change state diagram 5; pull down interface change state diagram 5 to interface change state diagram 6; pull down interface change state diagram 6 to interface change state diagram 7.

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 visual simultaneous localization and mapping (V-SLAM) system for a mobile host includes a camera for sensing and outputting image data indicative of features of interest in a surrounding environment of the host. A global navigation satellite system (GNSS) receiver determines a position of the host on a route as GNSS data. A controller localizes the mobile host on the route using the GNSS data and semantic sub-maps when a signal quality of the GNSS receiver exceeds a threshold. The controller selectively localizes the mobile host on the route using at least in part a crowdsourced three-dimensional (3D) point cloud map of the route when the signal quality does not exceed the threshold. The V-SLAM system communicates the image data and the GNSS data to a cloud-based backend architecture operable to generate the crowdsourced 3D point cloud map of the route.

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.

The invention relates to a multidimensional task matching method based on a homomorphic encryption technology in spatial crowdsourcing. A system applied by the method comprises a service provider, a cloud server, a plurality of workers and at least one data requester, wherein the trust center and the service provider are in communication connection with the data requester and the workers; and the cloud server is in communication connection with the service provider and the trust center. According to the method, a multi-dimensional privacy matching protocol based on homomorphic encryption is applied, the homomorphic encryption technology and multiple protocol designs are organically combined, privacy matching of multi-dimensional attributes of tasks and workers in a spatial crowdsourcing system is achieved, the safety of privacy information of the tasks and the workers is greatly improved, privacy risks caused by data decryption are avoided, and the privacy matching efficiency is improved. And the accuracy and privacy of the matching process are ensured.

The present disclosure relates to a system (200) and a method (300) for tagging serving cells (110) to crowd source data in a communication network (130). The method (300) comprises receiving, at a server, a plurality of crowd source records from a plurality of serving cells (110). Further, at least one crowd source record with absence of serving cells identifiers is identified from the plurality of crowd source records. Furthermore, location information of each of the plurality of crowd source records may be determined. Furthermore, it may be determined whether a crowd source record of the one or more crowd source records falls within a boundary of a Best Server Plot (BSP) of the serving cells. Moreover, a serving cell identifier may be assigned to the crowd source records based on the determination that the crowd source record falls within the boundary of the BSP.

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.