121 results about "Crowd sourcing" patented technology

The invention discloses a collaborative secret state task matching method based on edge calculation in mobile crowdsourcing. The method comprises the steps of system initialization and key generation, requester-cloud platform registration authentication, worker-edge server registration authentication, login authentication and attribute submission, requester task issuing, cloud platform task delegation, secret state matching, worker task content decryption, worker answer submission, answer forwarding and decryption. According to the invention, cooperative authentication of workers and requesters and precise task matching based on attributes are realized under an edge-cloud architecture. A fuzzy extractor and an authentication encryption method with associated data are introduced, so that low-cost user local authentication and registration and authentication of a joining system are realized, the security is improved, and the calculation cost is reduced. Through function hidden inner product encryption and a Paillier cryptographic algorithm, task-worker matching and task answer submission are realized in a ciphertext state, and the requirements of a mobile crowdsourcing application scene with high safety, high privacy and dispersed cost are met.

The invention provides a logistics transportation path intelligent planning system and method, and the method comprises the steps: obtaining an initial logistics transportation path through the planning of a transportation path of a target jurisdiction; determining node aggregation density corresponding to the transportation path nodes according to neighborhood association characteristics of the transportation path nodes; determining a node distribution association entropy of the target jurisdiction based on the node aggregation density and the node distribution rate corresponding to each transportation path node, and determining crowdsourcing distribution times of the target jurisdiction and node distribution gravitation corresponding to each transportation path node according to the node distribution association entropy; according to a mode of performing track redistribution on an initial logistics transportation path based on crowdsourcing distribution times of a target jurisdiction and node distribution gravitation of each transportation path node to obtain an intelligent logistics transportation track, distribution path redistribution can be performed based on node distribution association entropy and node distribution gravitation of the target jurisdiction; and the balance of path resource allocation in the crowdsourcing distribution mode is improved.

A system may include a non-transitory computer-readable storage medium storing instructions for execution and one or more processors that execute the instructions. The instructions may cause the one or more processors to configure at least one parameter of a crowdsourcing request related to obtaining information relating to a collateral for a loan, publish the crowdsourcing request related to obtaining the information relating to the collateral for the loan to a group of information suppliers, collect and process a response from an information supplier of the group of information suppliers, where the response includes information on a condition of the collateral for the loan, process the response provided by the information supplier to determine whether an information supply event relating to the response is successful, and respond to a determination of a successful information supply event.

The present disclosure is related to a roadside assistance system that may utilize crowdsourcing to collect and monitor user data, such as user input and user behavior, to build a machine learning model. In response to receiving a request for roadside assistance from a driver, the roadside assistance system may utilize the machine learning model to identify a suitable user who may provide the requested roadside assistance within a suitable time, and guide the identified suitable user to a location of the requesting driver to perform the requested roadside service. Furthermore, the roadside assistance system may direct suitable user to supply locations to pick up certain supplies for the requested roadside service. Additionally, the roadside assistance system may utilize crowdsourcing to provide rental vehicles to users.

The invention discloses a road traffic sign updating method based on crowd source data, and belongs to the field of road traffic sign updating, and the method comprises the steps: processing vehicle-mounted video image data through employing a pre-trained deep learning model, recognizing the type and attribute of the road semantic information of a traffic sign in a video, and obtaining the space coordinate of the traffic sign; performing spatial registration on the identified information of the traffic signs and an open source road data map; calculating a sign similarity value of the traffic sign after space registration, and judging whether the newly added traffic sign replaces the existing traffic sign or the newly added traffic sign; and generating an inference map after the traffic signs are updated, and updating the open source road data map. According to the method, the updating of the road traffic sign information is promoted, the updating efficiency and accuracy of the map data are remarkably improved, richer and more timely semantic support is provided for applications such as automatic driving, and the accuracy of traffic sign positioning and the consistency of semantic information updating are ensured.

The invention provides a crowdsourcing rider intelligent order sending method and system based on space-time collaboration, and the method comprises the steps: calculating a service capability score of each rider in a future time period and a specific geographic position, and a time aggregation value of the service capability score; aggregating the service capability scores of the plurality of riders in the target area, and constructing an area serviceability tensor of the target area; obtaining an order demand density tensor of the current target area, and constructing a scheduling priority tensor and a scheduling priority area set of each target area; obtaining a current order, constructing a scheduling score for each candidate rider, and evaluating the matching priority of the current candidate rider for executing the current order; and sorting the scheduling scores of all the candidate riders, selecting the rider with the highest score, and pushing the current order to the candidate rider client with the highest score through an order distribution system to complete task distribution. According to the method, the key problems of supply and demand mismatching, outbreak lagging, unstable supply, uncontrollable distribution and the like in a crowdsourcing scene are systematically solved.

The embodiment of the invention provides a vehicle control method and device, equipment, a vehicle, a storage medium and a program product. The method comprises the steps of obtaining map information of a scene where a vehicle is located in response to real-time traffic information of the scene where the vehicle is located; matching the road abnormal area change data in the real-time traffic information with the map information; if it is determined that the road abnormal area change data is not matched with the map information, obtaining updated map information from the cloud; wherein the updated map information is obtained by performing map reconstruction processing based on crowdsourcing data of a scene where the vehicle is located; controlling the vehicle to drive to a drivable area according to the updated map information; and secondary scene verification processing is carried out in the vehicle driving process so as to control the vehicle to drive along the driving area. According to the method, the road abnormal area can be recognized in advance in the beyond-visual-range so as to ensure the driving safety.

A method and system of feature matching for associating vehicle perception data for crowd sourcing map data. The system is configured to execute the method including the steps of receiving perception data from a plurality of vehicles; processing the perception data to identify first feature points observed by a first vehicle and second feature points observed by a second vehicle, respectively; creating feature descriptors using multilayer bitmaps for each of the first feature points and for each of the second feature points; determining pairs of matching first and second feature points by comparing histograms of the feature descriptors; associating pairs of matching first and second feature points; updating a map database with the associated pairs of matching first and second feature points; and operating an autonomous vehicle based on the updated map database.

A method for identifying perishable products in a retail store and for automatically generating offers relating to the identified products is provided. The method may include receiving a set of images depicting perishable products of at least one product type displayed on at least one shelving unit in a retail store. The method may also include analyzing the set of images to determine information about a displayed inventory of the perishable products. The method may further include accessing a database storing information about the at least one product type. The method may also include using the information about the displayed inventory and the stored information about the at least one product type to determine at least one offer associated with the at least one product type. The method may further include providing the at least one offer to at least one customer of the retail store.

The present invention relates to a method and a device of inputting annotation of object boundary information, and more particularly, to a method and a device of inputting annotation of object boundary information such that workers or the like efficiently and accurately input object boundary information in a service such as crowding sourcing, and preliminary work is assisted by an artificial neural network-based algorithm.

Aspects presented herein may enhance the accuracy and / or latency of UE positioning based on crowd-sourcing, where a network entity may compute a position estimate of a UE based on neighbor-cell scan data from the UE and one or more reference UEs. In one aspect, a network entity receives a first set of measurements associated with at least one cell from a UE. The network entity performs a position estimation of the UE based on at least one of the first set of measurements associated with the at least one cell, a second set of measurements for each of a set of reference UEs, or a location of each of the set of reference UEs via an ML model, where the UE and the set of reference UEs include at least one common cell.

The application discloses a kind of knowledge enhanced large model driven crowd-sourcing test report priority sequencing methods, including clustering deduplication module, knowledge empowerment classification module and intelligent sorting module.Cluster deduplication module utilizes Emb e Adding model converts report text into high-dimensional vector, and representative reports are selected by calculating cosine similarity clustering deduplication and reducing subsequent processing cost.Knowledge empowerment classification module extracts entities, expands knowledge graph and performs TF-IDF matching classification on representative reports to generate accurate classification results and classification evidence.Intelligent sorting module sorts representative reports through Graph RAG, and then expands non-representative reports through cross-pollination strategy, aiming to reduce Token cost, improve sorting diversity and defect discovery capability.

An approach is provided for determining driver stopping locations from crowd sourced data. The approach, for instance, involves processing vehicle drive data to determine aggregated vehicle drive geometries. The approach also involves associating and clustering the aggregated vehicle drive geometries into vehicle drive path segments. The approach further involves joining the vehicle drive path segments in a drive path aggregation model using connectivity coherence of each vehicle drive path segment. The approach further involves aggregating stop data representing drives that stop within a specific range of the node, the segment, or a combination thereof of one or more vehicle drive path segments, and providing the stop data as an output.

A trip planning and reservation system and method is disclosed that may include a U.I. software application executing on a computing device (e.g., mobile device). The U.I. software application may transmit the user account information to an external server. The external server may validate the user account information and transmit an access token to the mobile device. An AI algorithm may be operable to predict and select the plurality of digital media files based on operational data that includes the user account information and one or more user interest tags. It is contemplated that the digital media files may include geographic coordinate data and informational data about a location within a pre-defined proximity of the desired travel route. A first file may then be selected from the plurality of digital media files for inclusion within the desired travel route displayed by the U.I. software application.

Systems and methods are provided for generating a crowd-sourced map for use in vehicle navigation. In one implementation, a system may include at least one processor configured to receive drive information collected from vehicles that traversed a junction; aggregate the received drive information to determine positions of traffic lights and spline representations for drivable paths; input the determined positions and the spline representations to a trained model configured to generate a traffic light relevancy mapping indicating a traffic light relevancy for traffic light to drivable path pairs of the junction; input an observed vehicle behavior to the at least one trained model to generate an updated traffic light relevancy mapping; store in the crowd-sourced map the indicators of traffic light relevancy for the traffic light to drivable path pairs; and transmit the crowd-sourced map to a vehicle for use in navigating the road segment.

In one aspect, a method for rendering of a 3D aggregate image from crowd sourced image data is provided. The method includes receiving, at a server, from each of a plurality of user devices, user global multi-layer perceptron (MLP) weights generated from one or more images of a shared scene. The user global MLP weights are generated so as to not include personal content of a user. The method also includes aggregating the user global MLP weights using secure multi-party computation (SMPC) to further ensure exclusion of personal content. The method also includes sending, from the server to the plurality of user devices, updated weights, wherein the updated weights comprise aggregated global MLP weights. The user devices may then use the updated weights to further help in the implicit separation of personal and global content while retraining of their respective weights on local image data.

The system's methodology combines a variety of information and communication technologies (ICT) tied to global immigration patterns—specifically connecting native and expatriate populations—to create a cross-cultural, “fusion music” listening experience. The system will foster this experience by creating music-based social networks that function as feedback loops between native and expatriate communities, generating crowd-sourced ‘music intelligence’—a means to identify hit songs in listeners' native languages and the promote, and accelerate, the popularity of these songs overseas in myriad foreign-language music markets. To promote songs worldwide, the system will track streams and rank songs (and podcasts) by the number of times listeners stream them, concurrently and asynchronously, on native and foreign-language platforms, and in a multiplicity of languages.

The present invention relates to the technical field of crowdsourcing task recommendation, and discloses a crowdsourcing task recommendation method based on ability evaluation, including: evaluating deliverymen through two dimensions of on-time ability and the ability to assist in order completion; the on-time ability evaluation determines the on-time rate by analyzing the time consistency of deliverymen arriving at each test site, while the ability to assist in order completion is rated according to the proportion of deliverymen completing assisting orders; judging whether there are assisting orders, and when there are, screening suitable deliverymen to perform assisting tasks according to the above two ability evaluations; during the screening process, multiple factors such as the coincidence degree of delivery stations, the requirements for meeting the delivery time, and the ability ratings of deliverymen are considered. Finally, the optimal deliveryman is selected to accept and complete the assisting request.

A system may receive drive information from each of a plurality of harvesting vehicles that traversed a road segment and provide a representation of the drive information as input to a trained model. The trained model may be configured to determine that at least a first sub-portion of a first trajectory representation and second sub-portion of a second trajectory representation correspond to a common road interval included in a road segment and output an indication of a correlation between the first sub-portion, the second sub-portion, and the common road interval, align and aggregate the drive information associated with the common road interval based on the output of the trained model to generate a location indicator for at least one map feature of the road segment, store the location indicator for at least one map feature in the map, and distribute the map

The invention discloses a method for calligraphy restoration based on crowdsourcing, comprising separating complete structure fonts and incomplete structure fonts from a calligraphy image to be restored, restoring the incomplete structure fonts using an expert crowdsourcing method to obtain a restoration font set, selecting restoration fonts similar to the incomplete structure convex hull contour through a convex hull contour feature module to obtain a second restoration font set, selecting a third restoration font set similar to the complete structure stroke feature from the second restoration font set through a stroke feature module, selecting a final restoration font similar to the incomplete structure key position area from the third restoration font set through a font structure distribution feature module, and completing the restoration of the incomplete structure font. The method requires less real data and has a high restoration efficiency. The fonts restored using the method have better calligraphy font detail features.

A method and system for last-mile product delivery is disclosed. The system allows each delivery-service provider of a platform of crowd-sourced delivery-service providers, to register vehicle specifications and delivery preferences including maximum delivery per trip. The system receives delivery orders within a predetermined time window, and dynamically determines delivery regions based on real-time factors of the delivery orders. Compatibility scores are determined by matching product handling requirements of the delivery orders with registered vehicle specifications. Multiple sequential delivery tours for individual delivery-service providers are generated within the single time window based on delivery-service provider specified maximum deliveries per trip, the compatibility scores, and the dynamically determined delivery regions. Finally, delivery assignments to the selected delivery-service providers are triggered based on the generated delivery tours.

The invention belongs to the technical field of big data, and particularly relates to a crowdsourcing human platform payment security system based on multi-dimensional risk control, and the system comprises a packet receiving party collection verification module which is used for carrying out identity verification and dynamic transaction verification; the hierarchical transaction restriction engine comprises an evaluation sub-module and a risk control threshold module, and the evaluation sub-module performs evaluation by using a multi-dimensional credit scoring model to obtain a credit score; the risk control threshold submodule is used for performing dynamic threshold calculation in the aspect of risk control from multiple aspects of industry, region and holiday type, and performing credit rating and limit control based on the credit rating by comparing a credit score with the dynamic threshold; the account transfer monitoring system is used for monitoring account transfer behaviors; the system also comprises a geographic position and IP verification module, and a system for verifying the consistency between a packet receiving party and a payee. According to the invention, accurate risk identification and dynamic management and control are realized through technology fusion.

The invention provides an e-commerce order self-operation and crowdsourcing transport capacity integrated distribution method and electronic equipment. The method comprises the steps of obtaining a to-be-allocated order set, a commodity set, inventory data, transport capacity data and time window data of an e-commerce platform; constructing a cost optimization model of the to-be-allocated order set, wherein the cost optimization model comprises a front transport capacity cost parameter, a self-operation transport capacity cost parameter and a crowdsourcing transport capacity cost parameter; determining a decision variable and a target constraint condition of the cost optimization model; based on the to-be-allocated order set, the commodity set, the inventory data, the transport capacity data and the target constraint condition, solving a decision variable of the cost optimization model to obtain a target variable value of the decision variable; and outputting a target allocation strategy of the to-be-allocated order set based on the target variable value of the decision variable. According to the invention, the overall timeliness of the order set can be improved, the overall delivery cost is reduced, and the order timeliness default problem is reduced.

The application discloses a multi-index optimization space crowd sourcing task allocation method based on a genetic algorithm, and steps of the method comprise the following steps: 1) obtaining arrival time and position information of a task and arrival time and position information of a worker; 2) constructing a task allocation model for a space crowd sourcing platform; 3) realizing and improving application of the genetic algorithm in the space crowd sourcing, and obtaining an optimal task allocation scheme. The application can find the optimal task allocation scheme of task allocation through continuous iteration, so that the efficiency and utility of task allocation of the space crowd sourcing platform can be improved.

The application provides a kind of urban land use automatic classification method and system, method includes: based on open source crowd sourcing geographic data production wide range land use training sample;Uncertainty perception context deep learning semantic segmentation network of end-to-end is constructed, and pixel-level land use classification product is obtained;Based on the segmentation of the remote sensing image to be classified by the bottom-up multi-scale segmentation algorithm, the pixel-level land use classification result is fused with the object-level segmentation boundary based on the mode vote, and the object-level land use classification product is obtained.The application is based on deep learning to automatically classify land use types, improve the classification accuracy, and save the labor cost.

A vehicle driving assistance system includes a plurality of sensors sensing characteristics of a roadway proximate to the vehicle. A plurality of data sets includes crowd sourcing data and user feedback data providing known past characteristics of the roadway. A human machine interface provides an output with vehicle maneuvering guidance for the vehicle. A controller processes the sensed characteristics of the roadway and the known past characteristics of the roadway and generates the maneuvering guidance based on the sensed characteristics of the roadway and the known past roadway characteristics.

Disclosed is a system (100) and a method (600) for classifying a user environment in a communication network. Crowd source data associated with User Equipment (UEs) (110) is periodically collected. A presence of the UE (110) is estimated in one of an indoor environment or an outdoor environment based on a positional information of the UE (110) and identifier of serving cell serving the UE (110) from the crowd source data. By correlating one or more parameters corresponding to the crowd source data, the certainty of the estimation is determined. Based on the certainty of the estimation, the user environment of the UE (110) is classified as one of the indoor environment or the outdoor environment.

Infrastructure and methods to implement a platform for a horticultural operation are disclosed. Sensor data is received from one or more sensors configured to capture data for plants within a plant growth operation. Accumulated data associated with other plants in other plant growth operations is access. The data is analyzed to determine conditions of the plants within the plant growth operation. Plant grower actions to improve plant growth are determined. Instructions are transmitted to a controller device associated with the plant growth operation. Agricultural products or services associated with the plant grower actions are determined. An agricultural exchange service processes electronic commerce information from servicers of the products or services. Bids from the servicers are received, and selection and fulfillment of the bids are facilitated.

A method comprising identifying a content item having stated facts and / or opinions which a user is unsure whether to trust; during a first time period, obtaining first content evaluations of the content item from first users, each first content evaluation evaluating a belief state of the stated facts and / or opinions; displaying the first content evaluations; during a second time period initiating later than the first time period, obtaining second evaluations from second users regardless of second user expertise, the second users defined as having hindsight and thus knowledge as to the belief state of the stated facts and / or opinions greater than the first users; generating and issuing expert scores for the first users based on the second evaluations; and elevating an influential characteristic of each first user having an expert score higher than a certain threshold when providing another evaluation during another time period.

The invention discloses a new energy automobile charging power grid load prediction method based on a crowdsourcing feature selection genetic algorithm, and the method comprises the following steps: initializing crowdsourcing worker nodes, and distributing a crowdsourcing data collection budget for each worker node; performing data cleaning and feature construction on the collected new energy charging load historical data; executing a feature selection optimization task by each worker node, constructing an objective function of the feature selection optimization task, and performing parallel search and fitness evaluation on a feature subset based on a multi-objective genetic algorithm; outputting a local optimal feature solution set after iterative optimization of a multi-target genetic algorithm; aggregating the local optimal feature solution set to obtain a solution set of the crowdsourcing task for load prediction of the new energy charging station; and performing repeated detection on the solution set of the crowdsourcing task, removing repeated solutions, and performing non-dominated sorting on all reserved solution sets to obtain an optimal feature subset in the new energy charging station load prediction task. According to the invention, the precision and generalization ability of the new energy load prediction model can be improved.