Abstract: A method for estimating movements of a crowd between a first and second subregion in an area are monitored by a wireless sensor network. The wireless sensor network includes nodes configured to exchange a radio frequency signal through a first respective second link. The first respective second link crosses the first respective second subregion. The method includes the steps of exchanging radio frequency signals over the first and second link; and measuring respective first and second attenuations of the exchanged radio frequency signals over the first respective second link; and estimating based on a change in the attenuations a flow of the crowd between the first and second subregion. The estimating further includes estimating based on the first and second attenuations a density of the crowd in the first respective second subregion; and estimating based thereon a flux of the crowd between the first and second subregion.

Abstract: A system and method may retrieve transaction data for a plurality of electronic payment system transactions. The transaction data may describe a plurality of electronic payment transactions between a payment network system and a plurality of merchants. The transaction data may include a transaction location and a merchant store number. The system and method may parse the transaction data to identify the transaction location and the merchant store number and analyze the retrieved and parsed transaction data to identify one or more centroids for at least a portion of the retrieved and parsed transaction data. The one or more centroids may correspond to a merchant location for each transaction of the retrieved and parsed transaction data. The system and method may then clean the retrieved and parsed transaction data having a transaction location outside a threshold distance from the one or more centroids to include accurate merchant store number data.

Abstract: Provided is a method for computer-implemented determination of a data-driven prediction model. The method processes digital input data having input and output variables and being semantically annotated based on a digital semantic representation having a hierarchical tree structure where each tree in the structure represents an input variable of the data, the leaf nodes of the respective tree being the discrete values of the input variable. The method of the embodiment provides a recoding of those discrete values by cutting off hierarchical levels of the respective trees. Based on this recoding, a plurality of data modifications is determined for the input data. Those data modifications are trained by a machine learning method where the trained machine learning method with the highest prediction quality is derived from the trained machine learning methods.

Abstract: An integrated energy optimizer having an edge side and a cloud side. The edge side may incorporate an energy optimizer, a building management system connected to the energy optimizer, a controller connected to the building management system, and equipment connected to the controller. The cloud side may have a cloud connected to the energy optimizer and to the building management system, and a user interface connected to the cloud. Data from the field sensor may go to the optimizer and the building management system. The data may be processed at the optimizer and the building management system for proper settings at the building management system.

Abstract: Disclosed herein are a system and method for recommending repeat-purchase products using modified self-similarity. The system is operated by a computer in an environment in which user equipment (UE), a database, and a web server are connected over a network.

Abstract: A system is provided that is capable of processing clinical trial data from one or more sources, both structured and unstructured data, and integrate such data in a database for access by users. Further, such trial data is combined with other types of data to train an AI-based model, for the purpose of determining a probability of a particular trial outcome, among other insights. Further, the system can provide insights into optimizing this probability, for example by optimizing elements of the clinical trial design. Further, the system stores the acquired data from multiple sources and provides search, comparison, and reporting capability within a single interface.

Abstract: A method of training a neural network to approximate a forecasting error of a passenger-demand forecasting model that includes calculating, using the forecasting model, a historical passenger demand forecast for each key level in a set of key levels and for each departure date in a set of historical departures dates; applying a dropout model to the historical passenger demand forecasts to create a training sample; training, using the historical passenger demand forecasts and the training sample, the neural network, to approximate forecasting errors associated with the forecasting model; calculating, using the forecasting model, a future passenger demand forecast for each key level in the set of key levels and for each departure date in a set of future dates; and approximating, using the trained neural network, the forecasting error associated with the future passenger demand forecasts for the second set of departure dates.

Abstract: A system and method for autogenerated synthetic operational customer satisfaction scoring and analysis, that generates synthetic calls and follows customer interactions across multiple channels as a single journey, which monitors synthetic calls for a plurality of failure events that may represent infrastructure related operational deficiencies that may adversely affect customer satisfaction and sentiment, which assigns a synthetic operational customer satisfaction score for each synthetic call, and which uses machine learning to optimize infrastructure configurations to improve customer satisfaction and sentiment.

Abstract: In accordance with some embodiments, a method for generating sentimental information associated with media content is provided, the method comprising: receiving user input data corresponding to a user; identifying a portion of the media content item based on the user input data; determining a sentiment based on the user input data, wherein the sentiment is one of a positive sentiment which indicates that the media content item was liked by the user or a negative sentiment which indicates that the media content item was disliked by the user; determining an amount of the sentiment based on the user input data; associating the amount of the sentiment with the portion of the media content item; and generating, for the media content item, sentimental information that indicates the amount of the sentiment associated with at least one portion of the media content item.

Abstract: Methods, systems, and media for generating a media quality score associated with the presentation of a content item are provided.

Abstract: Certain aspects involve providing automated performance monitoring of statistical models. For example, a processing device is used for performing a statistical analysis on information in an archive to extract historical data, scores, and attributes. The processing device calculates performance metrics based at least in part on the historical data, scores, and attributes. The processing device pre-calculates summary performance data based at least in part on the performance metrics. The summary performance data is stored in files with predefined layouts, which are stored in a non-transitory, computer-readable medium. Segmented data is presented from a file to a user through a graphical user interface (GUI). In some aspects, various reports of the segmented data are presented interactively by detecting a selection by the user of a segmentation and displaying the corresponding segmented data.

Abstract: In an embodiment, described herein is a system and method for creating a suggested task set to meet a target value. A cloud server, in response to receiving a request specifying a target value, retrieves completed task sets from a database. Each completed task set includes a same set of task categories. The cloud server derives a number of ratios from the retrieved completed task sets, including a composition ratio and a conversion rate for each task category, and an addition ratio for the number of completed task sets. Based on the derived ratios and the specified target value, the cloud server constructs the suggested task set, and displays in real-time the suggested task set together with current values for the task categories. The cloud server alerts users of a discrepancy between a current value and the corresponding suggested value for a task category when the discrepancy reaches a predetermined level.

Abstract: Methods, systems, and computer-readable media, and apparatuses for optimizing policy underwriting and insurance policy coverage are described herein. Insurance information related to a new homeowner insurance policy may be retrieved from one or more insurance system databases. The insurance information may comprise an insurance policy premium. An unrestrained rating plan premium may be determined based on a plurality of premium components. The plurality of premium components may comprise at least one multiplicative factor and at least one additive factor determined from the insurance information. An underwriting score may be determined based on the unrestrained rating plan premium and the insurance policy premium. In response to determining that the underwriting score fails to meet a predetermined underwriting score threshold, the new homeowner insurance policy may be rejected.

Abstract: The present disclosure relates to a method for predicting demand using a visual schema of a product, a device therefor, and a computer program therefor. The demand predicting method includes the operations of: creating visual schemas in which attributes of a product are digitized; analyzing the visual schemas and creating visual schema analysis data which are data relating to the attributes of the product; creating prediction data which are data obtained as a result of demand prediction analysis by attributes of the product using the visual schema analysis data; and creating visual narrative data expressing the prediction data into correlation between products or customers, and describing demand prediction.

Abstract: Embodiments forecast demand of an item by receiving historical sales data for the item for a plurality of past time periods including a plurality of features that define one or more feature sets. Embodiments use the feature sets as inputs to one or more different algorithms to generate a plurality of different models. Embodiments train each of the different models. Embodiments use each of the trained models to generate a plurality of past demand forecasts for each of some or all of the past time periods. Embodiments determine a root-mean-square error (“RMSE”) for each of the past demand forecasts and, based on the RMSE, determine a weight for each of the trained models and normalize each weight. Embodiments then generate a final demand forecast for the item for each future time period by combining a weighted value for each trained model.

Abstract: Apparatus and methods present a content item and question from an inquirer to a group of users in a first feedback session with a requirement for the users to provide predictions of how a member of a distinct target group will respond to the content item, receiving, during the first feedback session, the first group's prediction of the target group's responses, presenting, during a second feedback session, the same content item and the same question to the target group with a requirement for the target group to provide responses directed to their own responses, constructing a predictive model of the target group based on responses received from the target group during the second feedback session, measuring accuracy of the first group's predictions using the target group predictive model and providing the inquirer access to an ordered visual representation of the first group users arranged as a function of accuracy.

Abstract: Systems, methods, and storage media for pre-approving onboarding to a payment platform, executing on a processing device are disclosed. Exemplary implementations may: access a node network; determine a first entity associated with a first node of the plurality of nodes is not a member of a payment platform; determine a total strength of connections of the first node to one or more nodes of the plurality of nodes exceeds a predetermined threshold value; and flag the first node of the plurality of nodes for an offer of onboarding to the payment platform consequent to determining the total strength of connections of the first node to the one or more nodes of the plurality of nodes exceeds the predetermined threshold value.

Abstract: In accordance with an embodiment, described herein is a system and method for aggregating values through risk dimension hierarchies to create risk models in a multidimensional database. The system can include a cube that stores a plurality of standard risk measures organized into different risk dimension hierarchies, and a pluggable calculation engine executing a plurality of scripts to dynamically operate on any value in the cube. The system can aggregate values through the plurality of risk dimensional hierarchies, and either store an aggregated value against a dimension member, or dynamically calculate the aggregated value on demand. By aggregating values through the risk dimension hierarchies, a plurality of risk models can be built to measure a variety of risks.

Abstract: Disclosed are various embodiments for resource allocation policies for a hosted computing environment that can be configured by organizations that use the hosted computing environment under a utility computing model. In one embodiment, a resource allocation request to allocate a computing resource within a hosted computing environment is received. The resource allocation request is associated with an account of an organization. It is determined that a fulfillment of the resource allocation request would violate a resource allocation policy configured by the organization within the hosted computing environment. The resource allocation request is prevented from being automatically fulfilled in response to determining that the fulfillment of the resource allocation request would violate the resource allocation policy.

Abstract: A controller is provided that is configured to execute: predicting a distribution of first users in a predetermined area; predicting, based on the predicted distribution of the first users, a first point that is a point where a density of the first users exceeds a predetermined upper limit value in the predetermined area in the future; and transmitting, to a terminal of a second user who is a provider of a service, information about prompting provision of the service at a second point other than the first point in the predetermined area.

Abstract: An energy control and storage system includes an energy monitor, a power controller, an energy storage device, and a computing unit. The energy monitor monitors power provided between an electric distribution system and a load. The power controller exchanges power with the energy monitor and receives power from a power generation system. The energy storage device stores energy received through the power controller. The computing unit receives a load shape from outside the energy control and storage system. The computing unit controls power exchanged between the energy control and storage system and the electric distribution system based on power indicated by the load shape that changes in response to varying conditions affecting the electric distribution system.

Abstract: An information technology system taking the form of a management console, central server, point of sale system, software running on a point of sale system and software running on a customer's mobile phone. The software on the customer's mobile phone lets a customer interact with the point of sale system, allowing the customer to view a pending economic transaction, optionally add or remove items to the transaction, and pay for the transaction using payment information stored on the mobile phone.

Abstract: A method, apparatus, and computer-readable storage medium that generate, from a database, an identifier record grouping including the identifier record and at least one other identifier record, generate one or more strength indicators of the generated identifier record grouping, normalize a value of each of the one or more strength indicators based on an average value associated with the respective strength indicator, generate a value of a strength metric of the generated identifier record grouping based on a sum of the normalized values of the one or more strength indicators, rank the value of the strength metric amongst values of strength metrics associated with a plurality of identifier record groupings within the database, and determine a strength score of the generated identifier record grouping as the strength score of the identifier record based on the ranking.

Abstract: Machine learning segmentation methods and systems that perform segmentation quickly, efficiently, cheaply, and optionally provides an interactive feature that allows a user to alter the segmentation until a desired result is obtained. The automated machine learning segmentation tool receives all potentially important attributes and provides segmentation of items. It also receives information about important features of the data and finds how best to differentiate between groups using cluster-based machine learning algorithms. In addition, visualization of the segmentation explains to a user how the segmentation was obtained.

Abstract: An electronic apparatus includes a microphone, a speaker, a memory, and a processor. The processor obtains a product keyword from a text converted from a user voice that is input through the microphone, identify a product category related to the product keyword; determine whether to provide an advertisement related to the user voice based on an interest level of the user according to utterance history information and an advertisement fatigue level of the user; and based on a determination to provide the advertisement, obtain advertisement information based on a target product category identified according to the interest level of the user, and control the speaker to output an advertisement voice based on the advertisement information.

Abstract: A media level of each Self-Service Terminal (SST) within a customized group of SSTs are managed in real time based on a current media level and a predicted optimal media level for each SST over a given period of time. During management, depositors are proactively steered away from media heavy SSTs to media deficient SSTs within the group. Any given SST of the group may have deposits temporarily disabled based on real-time instruction. Depositors that are being redirected may be incentivized to encourage the depositors to deposit at the cash deficient SSTs of the group.

Abstract: Systems and methods for dynamic demand sensing in a supply chain in which constantly-updated data is used to select a machine learning model or retrain a pre-selected machine learning model, for forecasting sales of a product at a specific location. The updated data includes product information and geographic information.

Abstract: An auto-replenishment platform may receive retailer, manufacturer, and 3rd party consumer data on a regular time interval, via their e-commerce platforms. The auto-replenishment platform, via a harmonization engine, may aggregate all data sets, mine the aggregated data, and then cluster the data. Subsequently, the auto-replenishment platform may generate a consumer model for predicting the consumer demand for a product, factors that influence a consumer's perception of convenience or ease in purchasing that product, and for aggregating a consumer's purchased products for shipment or pickup. The auto-replenishment platform may send the consumer model to the retailer, manufacturer, and 3rd party e-commerce platforms to integrate the auto-replenishment platform into those platforms.

Abstract: A consumer's location relative to specific products situated within a store are tracked. Observed consumer behaviors are noted as well as return visits to a particular product area by the consumer while in the store. A notification is provided to an engagement manager and automated consumer assistance or product purchase enticements are provided to the consumer while the consumer remains in the store as automated actions processed in response to the consumer behaviors.

Abstract: The present disclosure involves systems, software, and computer implemented methods for proactively predicting demand based on sparse transaction data. One example method includes receiving a request to predict transaction dates for a plurality of transaction entities for a future time period. Historical transaction data for the transaction entities is identified for a plurality of categories of transacted items. The plurality of categories are organized using a hierarchy of levels. Multiple levels of the hierarchy are iterated over, starting at a lowest level. For each current level in the iteration, a plurality of transaction date prediction models are trained and tested. Heuristics for the plurality of trained transaction date prediction models are compared to determine a most accurate transaction date prediction model. The most accurate transaction date prediction model is used to make a prediction of transaction dates for the current level for the future time period.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for monitoring technology usage and performance. In some implementations, use of a technology item by one or more individuals assigned to use the technology item is monitored. Based on the monitoring, usage data that indicates usage of the technology item is generated. One or more criteria for evaluating the usage of the technology item by the one or more individuals is identified. It is determined whether usage data satisfies the one or more criteria. A system provides, for display on a user interface, output data indicating whether the usage data satisfies the one or more criteria.

Abstract: Embodiments described herein generate a first set of prompts, the first set of prompts configured to prompt a vehicle owner for a first set of answers used to learn preferred vehicle renter characteristics; receive the first set of answers; generate, based upon the first set of answers, a second set of prompts, the second set of prompts configured to prompt the owner for a second set of answers used to learn additional preferred vehicle renter characteristics; receive the second set of answers; predict user preference value(s) of a profile of the owner based upon the second set of answers, wherein the user preference value(s) define criteria for sharing a vehicle associated with the profile with vehicle renters who satisfy the criteria; apply the criteria to potential vehicle renters; and cause an indication of the vehicle to be displayed only to the potential vehicle renters who satisfy the criteria.

Abstract: A consumer's location relative to specific products situated within a store are tracked. Observed consumer behaviors are noted as well as return visits to a particular product area by the consumer while in the store. A notification is provided to an engagement manager and automated consumer assistance or product purchase enticements are provided to the consumer while the consumer remains in the store as automated actions processed in response to the consumer behaviors.

Abstract: Embodiments of the present disclosure are directed to specific, networked, and technological processes and operations. For example, various embodiments are directed to a distributed and collaborative planning tool that allows users to create events based on a presently-approved production plan, enable/disable events submitted by other users, submit proposals, approve proposals, and integrate proposals to update the presently-approved production plan. An event published by a first user is communicated to other users using a single, universal framework that tracks and communicates proposed changes between remote users and their associated client devices during the planning cycle without the need to store multiple iterations of full-size production plans.

Abstract: Systems, apparatuses, and methods are provided herein for in-store production management. A method comprises training a probabilistic demand model using the training dataset based on a machine learning algorithm, determining a probabilistic forecast of a demand for a perishable product at a store location on a date based on the probabilistic demand model, applying an objective function to each of the plurality of demand values in the probabilistic forecast to determine an objective value for each of the plurality of demand values, determining a target supply value based on a select demand value with a highest objective value, determining a production plan based on the target supply value and a carryover value retrieved from the store data database, and providing a production management user interface that comprises a display of an identifier of the perishable product and the production plan associated the perishable product.

Abstract: An out-of-stock indicator is received that indicates a product is out-of-stock or believed to be out-of-stock. Information about the features of the products and store are obtained or determined. The features are applied to a first machine learning model, which yields a probability that the item is out-of-stock. The obtained probability is compared to a threshold, and if the probability value is above a threshold, then the PI value is adjusted. If not above the threshold, then scans are monitored for out-of-stock conditions, and some time later the features will be applied to a different model, and the above-process repeated. In aspects, this process occurs over a certain time period or until the PI is adjusted.

Abstract: Machine learning segmentation methods and systems that perform segmentation quickly, efficiently, cheaply, and optionally provides an interactive feature that allows a user to alter the segmentation until a desired result is obtained. The automated machine learning segmentation tool receives all potentially important attributes and provides segmentation of items. It also receives information about important features of the data and finds how best to differentiate between groups using cluster-based machine learning algorithms. In addition, visualization of the segmentation explains to a user how the segmentation was obtained.

Abstract: In a cloud computing environment, a configurable transaction status interface of an enterprise computing platform enables application developers to customize applications efficiently. The configurable transaction status interface includes a global variable that is populated using a method for implementing a configurable status map that maps a transaction result to the global variable without having to customize the application. The configurable transaction status interface allows third party vendors of transaction gateways to use the configurable status map to map one or more of their gateway-specific transaction results to a generic status defined in the global variable. The vendors manage the configurable status map in their own namespace independently of the application with which they are interfacing.

Abstract: Various embodiments provide systems, methods, and computer program products for dynamically identifying and engaging at least one on-demand packaging customer in a consolidated fashion. The system comprises one or more computer processors configured to: receive qualification data associated with at least one customer; calculate whether the at least one customer is a qualified customer; in response to identifying the qualified customer, determine whether the qualified customer is a targeted customer; in response to identifying the targeted customer, evaluate a readiness of at least one facility operated by the targeted customer for engagement of an on-demand packaging configuration; in response to determining a positive facility readiness, generate one or more documents for execution by at least the targeted customer, the carrier, and the supplier; and upon execution of the one or more documents, facilitate implementation of the on-demand packaging configuration at the at least one facility.

Abstract: A design management apparatus (100) includes a conversion unit (12) and a test unit (13). The conversion unit (12) generates model information (403) that is a format of design information (303) being converted, the design information (303) being the design information (303) created in a process of mechanical design in an engineering chain, and generates model information (405) that is a format of design information (305) being converted, the design information (305) being the design information (305) created in a process of control design in the engineering chain. The test unit (13) associates the model information (403) and the model information (405) using entire reference information (22) that associates the model information (403) and the model information (405), and tests for consistency between the model information (403) and the model information (405) associated.

Abstract: A system and method may be provided for associating bio-signal data (e.g. EEG brain scan data) from at least one user with at least one music data item (e.g. song, or piece of music). By associating bio-signal data, or emotions determined therefrom, with music, the system may establish a data store of music associated with emotions. That database may then be leveraged upon determining that a user is feeling a particular emotion through an EEG scan. When a particular emotion is detected in EEG data of a user, the system may then respond based at least partly on the same or similar emotion being associated with one or more music data items in the system. For example, the system may recommend a particular song associated with the same emotion presently being experienced by the user.

Abstract: In one aspect, a computerized method of computer vision based dynamic universal fashion ontology fashion rating and recommendations includes the step of receiving one or more user-uploaded digital images. The method includes the step of implementing an image classifier on the one or more user-uploaded digital images, to classify a set of user-uploaded fashion content of the one or more user-upload digital images. The method includes the step of receiving a set of fashion rules input by a domain expert. The set of rules determine a set of apparel to match with the set of user-uploaded fashion content, generating a dynamic universal fashion ontology with the image classier and a text classier. The dynamic universal fashion ontology comprises an ontology of set of mutually exclusive attribute classes. The method includes the step of using the dynamic universal fashion ontology to train a specified machine learning based fashion classifications.

Abstract: A method for predictively updating one or more user parameters associated with a user of a learning system includes predicting, based on the one or more user parameters, a predicted activity of the user, receiving an actual activity of the user, comparing the predicted activity to the actual activity, and updating the one or more user parameters in response to determining that the predicted activity does not match the actual activity. The method may further include scheduling one or more learning interactions based on the one or more updated learning parameters, where the scheduling includes selecting at least one of a timing of the one or more learning interactions or a type of the one or more learning interactions.

Abstract: Systems and methods for features engineering, in which internal and external signals are received and fused. The fusing is based on meta-data of each of the one or more internal signals and each of the one or more external signals. A set of features is generated based on one or more valid combinations that match a transformation input, the transformation forming part of library of transformations. Finally, a set of one or more features is selected from the plurality of features, based on a predictive strength of each feature. The set of selected features can be used to train and select a machine learning model.

Abstract: A method comprising, by a processing unit and a memory: obtaining a training set of data; dividing sets of data into a plurality of groups, wherein all sets of data for which feature values meet at least one similarity criterion, are in the same group, storing in a reduced training set of data, for each group, at least one aggregated set of data, wherein, for a plurality of the groups, a number of aggregated sets of data is less than a number of the sets of data of the group, wherein the reduced training set of data is suitable to be used in a classification algorithm for determining a relationship between the at least one label and the features of the electronic items, thereby reducing computation complexity when processing the reduced training set of data, compared to processing the training set of data.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating a recommended target audience based on determining a predicted attendance utilizing a neural network approach. For example, the disclosed systems can utilize an approximate nearest neighbor algorithm to identify individuals that are within a similarity threshold of invitees for an event. In addition, the disclosed systems can implement an attendance prediction model to determine a probability of an invitee attending the event. The disclosed systems can further determine a predicted attendance for an event based on the individual probabilities. Based on identifying the similar individuals to, and the attendance probabilities for, the invitees, the disclosed systems can generate a recommended target audience to satisfy a target attendance for an event based on a predicted attendance for the event.

Abstract: Aspects of the present invention provide devices that identify a question in a text message of a chat session between a plurality of computing devices, wherein each of the plurality of computing devices includes an identifier of a user of a corresponding computing device in the text message, analyze digital data to determine a relationship between the users of the plurality of computing devices, construct search parameters for digital content external to the chat session according to the identified question and determined relationship, search for the digital content over a network according to the constructed search parameters to obtain search results, rank the search results according to the determined relationship, and distribute the ranked search results according to the determined relationship to at least one of the plurality of computing devices in an overlay of the chat session.

Abstract: A processor may receive workflow data associated with an environment having one or more smart devices. A processor may analyze the workflow data to identify one or more activities associated with the environment. A processor may generate an intelligent environment for one or more users using one or more intelligent environment (IE) devices. The one or more IE devices may be configured to collect user feedback from one or more users. A processor may modify at least one of the one or more activities in the environment based, at least in part, on the user feedback.

Abstract: Described are systems and processes for generating dynamic estimated time of arrival predictive updates for delivery of perishable goods. In one aspect a system is configured for generating dynamic estimated time of arrival (ETA) predictive updates between a series of successive events for real-time delivery of orders. For each order, a plurality of delivery events and corresponding timestamps are received from devices operated by customers, restaurants, and couriers. Based on the timestamps, the system generates a plurality of ETA time predictions for one or more of the delivery events with trained predictive models that use weighted factors including historical restaurant data and historical courier performance. As additional timestamps are received for a delivery event, the trained predictive models dynamically update the ETA time predictions for successive events. The predictive models may be continuously trained by updating the weighted factors based on the received timestamps.

Abstract: A method of smart urban public transport management, an Internet of Things system, and a storage medium are provided. The method of smart urban public transport management is implemented by the public transport management platform. The method of smart urban public transport management includes: obtaining the predicted pedestrian flow at a plurality of places through the place management platform; determining the target places where the predicted pedestrian flow is greater than the pedestrian flow threshold based on the predicted pedestrian flow at the plurality of places; obtaining the bus lines passing by the target places and adjusting the departure frequency of the bus lines. The Internet of Things system includes a user platform, a service platform, a public transport management platform, a sensor network platform, and an object platform. The method can be executed after the computer instructions stored in the computer-readable storage medium are read.